IPSL _Proceedings 2026

Proceedings of the Technical Sessions, 42 (2026) 37-46Institute of Physics, Sri Lanka 44Arm-Crossing Times and Orbital Dynamics in Simplified Spiral Galaxy ModelsThe spiral pattern speed is parameterized as a fixed fraction of the local angular speed.Ω? = pattern factor × Ω(??), (6)allowing the relative motion between stars and spiral arms to be controlled straightforwardly andenabling the exploration of different dynamical regimes.The arm-crossing time at the reference radius is then given by??????(??) =1?|Ω(??)−Ω?|, (7)At radii well inside or outside corotation, the relative angular velocity is large, and the crossing time is short. Near corotation, however, the relative motion becomes small and the time between successive spiral-arm encounters increases sharply. This behaviour highlights the fundamental role of corotation in regulating the duration of spiral-arm interactions. 3.5 Physical InterpretationThe behaviour of the arm-crossing time can be interpreted in simple physical terms. When the pattern speed is close to the local angular frequency (pattern factor ≈ 1), the reference radius lies near corotation such that Ωp ≈ Ω(Ro). In this regime, stars remain nearly phase-locked with the spiral pattern, and the arm crossing time formally diverges ?????? → ∞. This regime is particularly significant for resonant interactions and long-term orbital evolution.When the pattern speed is lower than the stellar angular frequency (pattern factor < 1), the spiralpattern rotates more slowly than the stars at Ro. As a result, stars move through the density wave and cross the spiral arms periodically. Increasing the number of spiral arms reduces the armcrossing time, as more arms are encountered during each orbital period.From the perspective of the interstellar medium, the arm-crossing time quantifies how frequently gas is subjected to compression within spiral arms. This timescale, therefore, plays a central role in determining how often shocks form, how long gas remains compressed, and whether gravitational collapse can proceed efficiently. In this sense, the arm-crossing time provides a natural link between large-scale spiral dynamics and local star-formation processes, as emphasized early in studies of spiral shocks and triggered star formation [11].4. RESULTS, INTERPRETATION AND LIMITATIONSThe results of these toy models are best viewed as a guide to physical intuition rather than as detailed predictions. Pitch angles, number of spiral arms, and pattern speed are among the few parameters that can be varied to clearly demonstrate how orbital dynamics and spiral geometry influence stellar behaviour in disk galaxies.Spiral morphology is directly and strongly affected by changes in pitch angle, leading to either tightly wound or more open spiral patterns. Similarly, two-armed spiralstend to retain coherence over a wide radial range, whereas spirals with multiple arms appear more fragmented. Althoughthese trends are built into the model by construction, their clear manifestation in configurationspace plots highlights how strongly global geometry influences the observed appearance of spiral galaxies.The orbital diagnostics further show that small radial oscillations about guiding-centre radii provide a good description of stellar motion. These oscillations illustrate how smooth

Proceedings of the Technical Sessions, 42 (2026) 37-46Institute of Physics, Sri Lanka 45Arm-Crossing Times and Orbital Dynamics in Simplified Spiral Galaxy Modelsgravitational potentials can generate structured, non-random orbital behaviour. Despite their modest amplitude, they give rise to a distinctive structure in phase space.A key result concerns the arm-crossing time and its strong dependence on radius. In particular, the asynchronization between stellar orbits and the spiral pattern is evident in the pronounced increase in arm-crossing time near the corotation radius. Stars cross spiral arms more oftenwhen they are away from corotation, and the interval between crossings decreases as the number of spiral arms increases.On a physical level, the results indicate that spiral arms primarily influence star formation by regulating the duration of gas compression rather than simply increasing density. Gas in twoarmed spirals may experience fewer but longer compression events, whereas gas in multi-armed spirals is compressed more often but for shorter durations.Because gas dynamics, self-gravity, feedback, and transient spiral evolution are not included, themodels’ minimal nature necessarily limits their realism. However, this simplicity also makes it easy to isolate and understand the fundamental roles of orbital dynamics and characteristic timescales. The results, therefore, provide a useful conceptual baseline for interpreting more sophisticated numerical simulations and for understanding how large-scale spiral structure can influence local star-formation processes.5. REFERENCES[1] J. Binney and S. Tremaine, Galactic Dynamics, 2nd ed. Princeton University Press, 2008.[2] C. Dobbs and J. Baba, “Spiral structures in disc galaxies,” Publications of theAstronomical Society of Australia, vol. 31, p. e035, 2014.[3] C. C. Lin and F. H. Shu, “On the spiral structure of disk galaxies,” The Astrophysical Journal, vol. 140, p. 646, 1964.[4] M. S. Fujii, J. Baba, T. R. Saitoh, J. Makino, E. Kokubo, and K. Wada, “The dynamicsof spiral arms in pure stellar disks,” The Astrophysical Journal, vol. 730, no. 2, p. 109,2011.[5] J. A. Sellwood, “The lifetimes of spiral patterns in disc galaxies,” Monthly Notices of the Royal Astronomical Society, vol. 410, no. 3, pp. 1637–1646, 2011.[6] J. A. Sellwood and R. G. Carlberg, “Spiral instabilities provoked by accretion and star formation,” The Astrophysical Journal, vol. 282, pp. 61–74, 1984.[7] J. A. Sellwood, “Secular evolution in disk galaxies,” Reviews of Modern Physics, vol.86, no. 1, pp. 1–46, 2014.[8] B. G. Elmegreen, “Star formation during galaxy formation,” in EAS Publications Series, vol. 51. EDP Sciences, 2011, pp. 59–71.[9] R. C. K. Jr., “Star formation in galaxies along the hubble sequence,” Annual Review of

Proceedings of the Technical Sessions, 42 (2026) 37-46Institute of Physics, Sri Lanka 46Arm-Crossing Times and Orbital Dynamics in Simplified Spiral Galaxy ModelsAstronomy and Astrophysics, vol. 36, pp. 189–232, 1998.[10] C. L. Dobbs and J. E. Pringle, “Age distributions of star clusters in spiral and barredgalaxies as a test for theories of spiral structure,” Monthly Notices of the RoyalAstronomical Society, vol. 409, no. 1, pp. 396–404, 2010.[11] W. W. Roberts, “Large-scale shock formation in spiral galaxies and its implications onstar formation,” The Astrophysical Journal, vol. 158, p. 123, 1969.[12] K. L. Masters, C. J. Lintott, R. E. Hart, S. J. Kruk, R. J. Smethurst, K. V. Casteels, W. C. Keel, B. D. Simmons, D. O. Stanescu, J. Tate, and S. Tomi, “Galaxy zoo: Unwindingthe winding problem,” Monthly Notices of the Royal Astronomical Society, vol. 487,no. 2, pp. 1808–1820, 2019.[13] R. Mushotzky, “Spiral arms (astro421 lecture 3),” 2018, lecture notes. [Online]. Available: https://pages.astro.umd.edu/ rmushotz/ASTRO421/A421? ??????????????2018? ??3. ???.[14] D. M. Elmegreen, “Properties of spurs in spiral galaxies,” The Astrophysical Journal, vol. 242, pp. 528–532, 1980.[15] NASA/IPAC Extragalactic Database, “Spiral structure,” California Institute of Technology,[Online].Available: https://ned.ipac.caltech.edu/level5/STRUCTURE/spst.html. Accessed: Dec. 12, 2025.[16] Bovy, “Close-to-circular orbits: The epicycle approximation,” Dynamics and Astrophysics of Galaxies (online textbook), 2025, [Online]. Available: https://galaxiesbook.org/chapters/II-03.-Orbits-in-Disks3 − ????? − ?? − ???????? − ??????: −?ℎ? − ? ??????? −? ? ???????????.ℎ???.????????: ???.13, 2025.

Proceedings of the Technical Sessions, 42 (2026) 47-61Institute of Physics, Sri Lanka 47Synergistic Effects of MgO Passivation and CdS SILAR Cycle Optimization in TiO₂-Based Quantum Dot-Sensitized Solar CellsSynergistic Effects of MgO Passivation and CdS SILAR Cycle Optimization in TiO₂-Based Quantum Dot-Sensitized Solar CellsN. F. Ajward, J.V.P. Fernando, and V. P. S. PereraDepartment of physics, The Open University of Sri [email protected]. ABSTRACTQuantum dot–sensitized solar cells (QDSCs) offer a promising low-cost alternative to conventional photovoltaic technologies; however, their performance is often limited by interfacial losses such as charge recombination and sensitizer instability. In this study, the synergistic effects of MgO surface passivation and CdS SILAR cycle optimization on the photovoltaic performance of TiO₂-based QDSSCs were systematically investigated. The successive ionic layer adsorption and reaction (SILAR) technique enables controlled layer-bylayer CdS deposition. The number of deposition cycles was varied from 0 to 20. A comprehensive set of optical, structural, electrochemical, and photovoltaic characterizations was employed to elucidate the relationship between sensitizer loading, interfacial charge transport, and device performance.UV absorption and IPCE analyses were used to evaluate the enhancement and extended visiblelight photoresponse upon CdS sensitization, while MgO passivation improved spectral stability and charge collection efficiency. Electrochemical impedance spectroscopy showed a strong dependence of charge-transfer resistance and recombination dynamics on CdS cycle number, with minimum resistance observed at intermediate cycles. The optimal performance was achieved at 12 CdS SILAR cycles, achieving a maximum power conversion efficiency of 0.451%, with a short-circuit current density of 3.09 mA cm⁻² and an open-circuit voltage of 451.8 mV. Further increases in CdS loading resulted in performance degradation due to enhanced recombination and hindered charge transport.These findings highlight the critical role of interfacial engineering and precise sensitizer deposition control in balancing light harvesting and charge transport in QDSCs, offering an effective pathway for further performance enhancement.KeywordsQuantum dot-sensitized solar cells; CdS quantum dots; MgO passivation; TiO₂ photoanodes; SILAR deposition; charge transport; interfacial engineering2. INTRODUCTIONThe increasing global demand for sustainable and renewable energy sources has intensified research efforts toward the development of efficient and cost-effective photovoltaic technologies. Among emerging solar energy conversion systems, dye-sensitized solar cells (DSSCs) have attracted significant attention due to their low fabrication cost, simple processing, and flexibility in material design. However, the long-term instability of organic dyes and their limited absorption in the visible and near-infrared regions have restricted further improvements in DSSC performance, motivating the exploration of alternative sensitization strategies (Hagfeldt et al., 2010).

Proceedings of the Technical Sessions, 42 (2026) 47-61Institute of Physics, Sri Lanka 48Synergistic Effects of MgO Passivation and CdS SILAR Cycle Optimization in TiO₂-Based Quantum Dot-Sensitized Solar CellsQuantum dot–sensitized solar cells (QDSSCs) have emerged as a promising alternative to conventional DSSCs by replacing organic dyes with semiconductor nanocrystals. Quantum dots (QDs) offer several advantageous optoelectronic properties, including size-tunable band gaps, high molar extinction coefficients, and the potential for multiple exciton generation, which theoretically allows efficiencies beyond the Shockley–Queisser limit (Kamat, 2013; Mora-Seró & Bisquert, 2010). These characteristics make QDSCs attractive candidates for next-generation photovoltaic devices, particularly when combined with nanostructured wideband-gap semiconductors such as TiO₂.Among various quantum dot sensitizers, cadmium sulphide (CdS) has been extensively investigated due to its suitable band gap (~2.4 eV), strong absorption in the visible region, and favourable conduction band alignment with TiO₂, enabling efficient electron injection (Hodes, 2008). The successive ionic layer adsorption and reaction (SILAR) method is widely employed for CdS deposition because it enables layer-by-layer growth with precise control over sensitizer thickness and loading. Despite these advantages, CdS-sensitized systems suffer from several critical limitations, including severe interfacial charge recombination, photo corrosion under illumination, and instability at the photoanode–electrolyte interface, which collectively hinder device efficiency and durability (Kamat, 2013; Mora-Seró & Bisquert, 2010).To mitigate these challenges, surface modification and interfacial engineering of TiO₂ photoanodes have been extensively explored. In particular, passivation layers composed of wide-band-gap metal oxides such as MgO have proven effective in suppressing interfacial recombination by acting as electron-blocking barriers between TiO₂ and the electrolyte (Karthikeyan et al., 2017; Zhang et al., 2019). MgO modification can reduce surface defect states, improve band alignment, and enhance charge separation efficiency without significantly impeding electron transport. Several studies have demonstrated that MgO-coated TiO₂ electrodes exhibit improved open-circuit voltage and reduced recombination losses in sensitized solar cells (Ahmed et al., 2020; Rao et al., 2020).While the beneficial role of MgO passivation has been established, the combined influence of MgO surface modification and CdS sensitizer loading remains insufficiently explored. In particular, the number of CdS SILAR dipping cycles plays a critical role in determining sensitizer coverage, light-harvesting efficiency, and charge transport pathways. Insufficient CdS deposition results in weak absorption and low photocurrent, whereas excessive deposition can block TiO₂ pores, hinder electron transport, and accelerate recombination processes (Huang et al., 2018; Liu et al., 2020). Therefore, identifying an optimal sensitization level that balances light absorption and electronic transport is essential for achieving high-performance QDSSCs.In this work, we systematically investigate the synergistic effects of MgO passivation and CdS SILAR cycle optimization on the photovoltaic performance of TiO₂-based QDSSCs. TiO₂/MgO (7%) photoanodes were sensitized with CdS quantum dots using SILAR cycles ranging from 0 to 20, and their optical, structural, electrochemical, and photovoltaic properties were comprehensively analysed. By correlating current–voltage characteristics, electrochemical impedance spectroscopy, incident photon-to-current efficiency, and material characterization results, this study elucidates the interplay between sensitizer loading, interfacial charge recombination, and device performance. The findings provide valuable insights into interface-controlled optimization strategies for improving the efficiency and stability of QDSSCs.

Proceedings of the Technical Sessions, 42 (2026) 47-61Institute of Physics, Sri Lanka 49Synergistic Effects of MgO Passivation and CdS SILAR Cycle Optimization in TiO₂-Based Quantum Dot-Sensitized Solar Cells3. EXPERIMENTAL DETAILS3.1 Preparation of MgO/TiO2 Plates 0.0175 g of MgO powder was measured and then concentrated HNO3 acid which was added dropwise to the MgO powder and then ground using a mortar and pestle. TiO2 powder 0.25 g was subsequently mixed while adding, one drop of Triton X-100, and 0.05 g of PEG 1000. The resulting paste was spread onto pre-cleaned conducting tin oxide (CTO) glass plates (1.0 cm × 2.0 cm) using the doctor blade method. The cleaning process involved ultrasonic bath treatment with detergent and distilled water. Finally, the TiO2 coated films were dried on a hot plate and sintered in a furnace at 450 °C for 45 minutes.3.2 Deposition of CdS using SILAR method.CdCl2 and Na2S solutions were prepared using, 10.065 g of CdCl₂ and 3.902 g of Na₂S were weighed with an electronic balance (±0.001 g accuracy). These compounds were then dissolved in 100 ml of distilled water. To optimize the solubility of and enhance the electrochemical behavior within the cell, the pH of the CdCl₂ solution was adjusted to 4.5. This was controlled by adding 0.01 M NaOH or 0.01 M HCl solutions dropwise while monitoring the pH.CdS was deposited on TiO2 MgO plates by The SILAR method. Each dipping step into the CdCl2 and Na2S solutions lasted for one minute. After each dip, the photoanodes were rinsed with distilled water to remove any excess reagents, then air-dried. To ensure uniform film formation, the photoanodes were subsequently dipped into the opposing precursor solution (anionic or cationic), followed by another washing and drying cycle. It was repeated 4,6,8,10,12,14,16,18 and 20 times. Finally, the coated photoanodes were heated on a hot plate at 80 °C for 30 minutes to enhance the film's stability and adhesion to the substrate.3.3 Preparation of ElectrolyteA 2 ml of electrolyte solution was prepared by combining 0.1301 g of Na2S, 0.1283 g of S, and 0.0301 g of KCl and methanol (1.4 ml) and distilled water (0.6 ml) were then added as solvents. The mixture was stirred up to three hours using a magnetic stirrer to ensure complete dissolution of the components and prevent the precipitation of sulfur. The stirring time might require adjustment if sulfur precipitation is observed.3.4 Fabrication of the cell A counter electrode with a conductive side was placed face-to-face with the CdS coated TiO2film and secured with two clamps. The space between the electrodes was then filled with the electrolyte solution, completing the QDSSC assembly.3.5 CharacterizationThe fabricated QDSSCs were characterized using a VK-PA-100 PV Power Analyzer to measure J-V curves, extracting key performance parameters like power conversion efficiency (η), short-circuit current density (Jsc), and open-circuit voltage (Voc), and Fill Factor (FF) To analyze the charge transport and recombination processes within the fabricated CdS QDSSCs, Electrochemical Impedance Spectroscopy (EIS) was measured by a frequency response analyzer (Auto lab Nova 2.1). By measuring the impedance spectra across a frequency range of 0.1 Hz to 1 MHz under constant simulated light intensity at room temperature, to study the interfacial charge transfer resistance and recombination dynamics. Incident Photon-to-Current Efficiency (IPCE) measurements will be used to evaluate the lightharvesting efficiency of the QDSSCs across the solar spectrum. This information helps us to

Proceedings of the Technical Sessions, 42 (2026) 47-61Institute of Physics, Sri Lanka 50Synergistic Effects of MgO Passivation and CdS SILAR Cycle Optimization in TiO₂-Based Quantum Dot-Sensitized Solar Cellsunderstand the effectiveness of the QDs in capturing sunlight and converting it into photocurrent which was measured using a VK-IPCE-10 system. UV, FTIR XRD and SEM was used to obtain the Optical, Electronic Structural and Chemical Characterization of Optimized Films4. RESULTS AND DISCUSSION.4.1 Effect of MgO on TiO₂/CdS Photoanodes and Optimization of CdS SILAR Cycles4.1.1. I–V characteristics TiO₂/CdS and TiO₂/CdS/ MgOThe current–voltage (I–V) and power–voltage (P–V) characteristics of the TiO₂/MgO/CdS photoanodes sensitized with varying numbers of CdS deposition cycles (0–20) are presented in Figures 1 (a) and 1(b). And the photovoltaic parameters, open-circuit voltage (Voc), shortcircuit current density (Jsc), fill factor (FF), and conversion efficiency (η) extracted from these curves are summarized in Table 1.Here the low cycles (0–6), it has a low Jsc (0.80–1.31 mA cm⁻²) and reduced FF (24.7–33.0). It shows that insufficient CdS coverage leads to poor light harvesting and inefficient electron injection. According to Zhang et al, that inadequate QD loading limits exciton generation and increases interfacial recombination, therefore we get low values in less CdS layers. (Zhang et al., 2019; Ahmed et al., 2020).Figure 1: (a) Current-voltage characteristics of TiO₂/MgO/CdS photoanodes sensitized with varying numbers of CdS deposition cycles, under illumination of 100 mW/cm² light intensity.(b) Voltage-power characteristics of TiO₂/MgO/CdS photoanodes sensitized with varying numbers of CdS deposition cycles, under illumination of 100 mW/cm² light intensity.As the number of cycles increases to 12–16, the device performance significantly improves. The 12-cycle sample exhibits the highest fill factor (FF = 34.5), high short-circuit current density (Jsc = 3.091 mA cm⁻²), and a conversion efficiency of 0.451%, and it has recorded asthe highest among all samples. This enhancement is attributed to optimal CdS coverage, leading to balanced charge separation, reduced recombination, and efficient electron extraction. Similar optimal-cycle behaviour has been widely reported in SILAR-grown CdS QDSSCs, where moderate deposition cycles yield the best trade-off between QD loading and charge transport resistance (Liu et al., 2020; Huang et al., 2018).

Proceedings of the Technical Sessions, 42 (2026) 47-61Institute of Physics, Sri Lanka 51Synergistic Effects of MgO Passivation and CdS SILAR Cycle Optimization in TiO₂-Based Quantum Dot-Sensitized Solar CellsTable 1: Photovoltaic Parameters of TiO₂/MgO/CdS photoanodes sensitized with varying numbers of CdS deposition cyclesBut here in the table 1 shows that the 14–16 cycle samples exhibiting slightly higher Jsc values (up to 3.41 mA cm⁻²), but their FF decreases (28.0–31.2), resulting in lower overall efficiencies (0.408–0.433%). This can be happened because of excessive CdS accumulation which increases charge recombination, blocks electron pathways, and forms resistive grain boundaries effects. A similar observation has been described by Rao et al. (2020) and Wang et al. (2020). Overloading the TiO₂ surface can also induce inter-particle shading and reduced photovoltage generation.The Voc values remain in the range (410–510 mV), indicating that the MgO interlayer effectively stabilizes the band alignment and mitigates recombination losses across different CdS loadings. A similar Voc stabilization effect in MgO-modified TiO₂ electrodes has been reported by Karthikeyan et al. (2017).From the power voltage (P–V) curves, the maximum output power occurs consistently near the 12–14 cycle region, with the 12-cycle sample producing the highest peak power of 0.221 mW. This superior performance demonstrates that 12 cycles offer the best balance between optical absorption, interfacial charge transfer, and transport pathways. Thus, 12 CdS deposition cycles yield the optimum device performance in this study.4.1.2 Electrochemical Impedance Spectroscopy (EIS) ComparisonElectrochemical impedance spectroscopy (EIS) is used to investigate the charge transport, recombination dynamics, and interfacial properties of TiO₂/MgO/CdS quantum dot–sensitized solar cells (QDSSCs) prepared with different CdS SILAR cycles. Figure 2(a) Nyquist plot and Figure 2(b) Bode phase plots were recorded under illumination, and the spectra were fitted using an equivalent circuit model consisting of a series resistance (Rs) connected to a parallel combination of charge-transfer resistance (Rp) and a constant phase element (CPE), with a Warburg diffusion element (W) to account for ionic diffusion in the polysulfide electrolyte.Nyquist plot analysis and equivalent circuit parametersThe Nyquist plots exhibit a single dominant semicircle in the mid-frequency region for all devices, which is characteristic of charge recombination resistance at the TiO₂/CdS/electrolyte interface. The extracted Rs values in table 2 (302–1060 Ω) show only moderate variation among samples, indicating comparable ohmic contributions from the FTO substrate, contacts, and electrolyte. In contrast, the charge-transfer resistance Rp varies significantly with CdS deposition cycles and strongly correlates with photovoltaic performance.0 4 6 8 10 12 14 16 18 20Voc (mV) 510.9 503.5 461.1 410.4 422.7 451.8 428.7 432.8 436.7 509.5Jsc (mA cm-2) 1.054 1.255 1.314 1.608 2.019 3.091 3.41 3.285 2.265 1.641FF (%) 44.1 33.0 26.7 24.7 27.0 34.5 31.2 28.0 26.5 41.3η (%) 0.237 0.209 0.162 0.163 0.246 0.451 0.433 0.408 0.262 0.345Pmax(mW) 0.116 0.102 0.079 0.08 0.121 0.221 0.212 0.2 0.128 0.169

Proceedings of the Technical Sessions, 42 (2026) 47-61Institute of Physics, Sri Lanka 52Synergistic Effects of MgO Passivation and CdS SILAR Cycle Optimization in TiO₂-Based Quantum Dot-Sensitized Solar CellsFigure 2: (a) Nyquist plot of TiO₂/MgO/CdS photoanodes with varying numbers of CdS deposition cycles measured under AM 1.5G illumination at open-circuit voltage in polysulfide electrolyte at room temperature. (b) Bode phase plots of TiO₂/MgO/CdS photoanodes with varying numbers of CdS deposition cycles measured under AM 1.5G illumination at opencircuit voltage in polysulfide electrolyte at room temperatureUnder-sensitized photoanodes (4–8 cycles) exhibit large semicircle diameters and high Rp values (61.1–33.2 kΩ), reflecting severe recombination losses and inefficient charge extraction. As the number of CdS cycles increases, Rp decreases markedly and reaches a minimum for 12–16 cycles (≈11–15 kΩ), indicating enhanced interfacial charge transfer and suppressed recombination. Further increasing the CdS loading to 18–20 cycles results in an increase in Rp, suggesting that excessive quantum dot deposition leads to pore blocking, transport limitations, and renewed recombination losses. This trend is consistent with previous reports on QDSSC photoanodes, where an optimal sensitization level is required to balance light absorption and charge transport (Mora-Seró & Bisquert, 2010; Kamat, 2013).The CPE exponent (n) increases from 0.69 for 4 cycles to values close to unity (≈0.89–0.93) for optimized samples, indicating improved interfacial homogeneity and more ideal capacitive behavior as the CdS coverage becomes more uniform.Table 2. Parameters of equivalent circuit for different QDSC configurationParameters 4 6 8 10 12 14 16 18 20RS 930 1060 385 693 763 332 321 302 344RP ( kΩ) 61.1 49.3 33.2 16.2 15.4 11.1 11.5 16 26.4CPE (n) 0.69 0.876 0.931 0.874 0.887 0.925 0.931 0.894 0.893F peak (Hz) 3.41 7.94 1.85 2.51 10 1.17 0.76 54.12 1.99Peak Phase() 65.53 68.26 56.36 49.58 65.68 45.14 63.52 50.71 68.86Electron lifetime (s) Time0.0466 0.0200 0.0861 0.0634 0.0159 0.1370 0.2160 0.0029 0.0798

Proceedings of the Technical Sessions, 42 (2026) 47-61Institute of Physics, Sri Lanka 53Synergistic Effects of MgO Passivation and CdS SILAR Cycle Optimization in TiO₂-Based Quantum Dot-Sensitized Solar CellsBode phase analysis and electron lifetimeBode phase plots provide further insight into recombination kinetics through the determination of electron lifetime (τ), calculated using the relation:τ =12π f peakwhere f peak corresponds to the frequency at the maximum phase angle. The extracted lifetimes span more than two orders of magnitude, demonstrating the strong influence of CdS loading on charge dynamics.Under-sensitized electrodes (4–6 cycles) show relatively long electron lifetimes (≈0.02–0.05 s), which can be attributed to low electron density and slow recombination; however, these devices exhibit poor photocurrent due to insufficient light harvesting and limited carrier generation. The optimized device (12 cycles) presents a moderate lifetime (≈1.6 × 10⁻² s), representing an optimal balance between recombination suppression and efficient charge extraction. In contrast, over-sensitized photoanodes (18 cycles) display a very short lifetime (≈2.9 × 10⁻³ s), indicating accelerated recombination caused by excessive quantum dot coverage and hindered electron transport pathways.Notably, the longest electron lifetime is observed for the 16-cycle sample (≈0.216 s), yet this does not correspond to the highest photovoltaic efficiency. This highlights that maximum device performance is not achieved by maximizing electron lifetime alone, but rather by achieving a balance between recombination resistance, transport kinetics, and charge collection efficiency. Similar observations have been widely reported for dye- and quantum dot–sensitized solar cells (Bisquert et al., 2009; Fabregat-Santiago et al., 2011).Although the electron lifetime increases for higher CdS SILAR cycles, device efficiency does not follow the same trend because photovoltaic performance is governed by a balance between recombination suppression and charge transport. A longer electron lifetime indicates reduced recombination at the semiconductor–electrolyte interface; however, excessive CdS loading forms a thicker sensitizer layer that hinders electron diffusion through the TiO₂ network and increases transport resistance. This results in slower carrier extraction and reduced charge collection efficiency. Consequently, the 16-cycle sample exhibits the longest electron lifetime but lower efficiency due to transport limitations, whereas the 12-cycle sample provides an optimal compromise between recombination resistance and carrier mobility. Therefore, maximum device efficiency is achieved not by maximizing electron lifetime alone, but by balancing recombination kinetics with efficient charge transport pathways.4.1.3. Effect of SILAR Cycles on IPCE Spectral ResponseFigure 3 illustrates the incident photon-to-current efficiency (IPCE) spectra of TiO₂/CdS photoelectrodes fabricated with different CdS deposition cycles (4–20) over the wavelength range of 500–900 nm. This spectral behavior is characteristic of TiO₂-based photoelectrodes and reflects efficient charge generation at higher photon energies. In low-cycle samples (4 and 6 cycles), CdS-sensitized photoelectrodes show a clear enhancement and extension of photoresponse into the visible region up to ~850–900 nm, confirming the contribution of CdS sensitization to visible-light absorption and electron injection into the TiO₂ conduction band.

Proceedings of the Technical Sessions, 42 (2026) 47-61Institute of Physics, Sri Lanka 54Synergistic Effects of MgO Passivation and CdS SILAR Cycle Optimization in TiO₂-Based Quantum Dot-Sensitized Solar CellsFigure 3: Wavelength vs Photocurrent Efficiency (IPCE) for TiO₂/MgO/CdS photoanodes withvarying numbers of CdS deposition cycles.The photoelectrodes 14 and 16 cycles exhibit the highest peak IPCE values, reaching approximately 0.06–0.065%, indicating enhanced light harvesting under visible illumination. However, these samples also show a noticeable decline in IPCE beyond ~650–700 nm. And also, the 12-cycle photoelectrode has values of about 0.015–0.02% in the 550–650 nm rangeand a stable response of approximately 0.007–0.01% extending into the near-infrared region. So, it gives an idea 0f lower-cycle samples suffer from insufficient CdS coverage, resulting in reduced IPCE across the visible spectrum, while higher-cycle samples (18 and 20 cycles) display suppressed IPCE values, likely due to excessive CdS loading that promotes charge recombination and limits efficient charge transport.Based on the overall IPCE results, the 12-cycle TiO₂/CdS photoelectrode was selected as the balanced and optimized configuration, as it offers a favourable compromise between peak efficiency and spectral stability across the visible region. 4.3 Optical and Electronic Properties of Optimized Photoanodes4.3.1 UV Absorption of Optimized Sample and TiO2/CdSFigure 4. (a) shows the UV absorption spectra of TiO₂/CdS and the optimized TiO₂/MgO/CdS photoelectrodes. The TiO₂/CdS sample exhibits a strong absorption onset at approximately 520–540 nm, which is characteristic of CdS-sensitized TiO₂ systems and confirms the contribution of CdS to visible-light harvesting (Hodes, 2008). Upon MgO modification, the TiO₂/MgO/CdS photoelectrode demonstrates a noticeable increase in absorption intensity 00.010.020.030.040.050.060.07500 600 700 800 900IPCE(%)Wavelength (nm)46 814 1612 1018 2018111126 4

Proceedings of the Technical Sessions, 42 (2026) 47-61Institute of Physics, Sri Lanka 55Synergistic Effects of MgO Passivation and CdS SILAR Cycle Optimization in TiO₂-Based Quantum Dot-Sensitized Solar Cellsacross the visible region, along with an extended absorption tail reaching ~750–800 nm, indicating enhanced photon utilization.Figure 4. UV–V is absorption spectra of TiO₂/CdS and MgO-passivated TiO₂/CdS photoelectrodes, showing enhanced absorption intensity and extended visible-light response upon MgO surface modification.The enhanced optical response of the TiO₂/MgO/CdS electrode can be attributed to the surface passivation effect of MgO, which reduces interfacial defect states and suppresses non-radiative recombination (Mora-Seró & Bisquert, 2010). MgO does not contribute directly to visible absorption due to its wide band gap; instead, it modifies the interfacial electronic structure, leading to improved light absorption efficiency in the CdS-sensitized system. These optical improvements are consistent with the enhanced photoelectrochemical performance observed for the optimized electrode.4.3.2 Tauc Plot (Band Gap Estimation)The optical band gaps of TiO₂/CdS and TiO₂/MgO/CdS photoelectrodes were estimated using Tauc plots derived from UV–Vis absorption data, assuming indirect allowed electronic transitions by plotting (αhν)1/2 versus photon energy (hν) , as commonly applied for TiO₂-based systems (Tauc et al., 1966). As shown in Figure 4.X(b), the extrapolation of the linear region to the energy axis yields band gap values of approximately 2.066 eV for TiO₂/CdS and 2.109 eV for TiO₂/MgO/CdS.The slight widening of the band gap after MgO incorporation can be attributed to surface passivation and the reduction of mid-gap defect states, which sharpen the absorption edge (Zhang et al., 2011). Similar band-gap shifts have been reported in oxide-modified CdSsensitized photoelectrodes, where the introduction of a wide-band-gap interlayer improves electronic ordering and suppresses recombination without compromising visible-light absorption.

Proceedings of the Technical Sessions, 42 (2026) 47-61Institute of Physics, Sri Lanka 56Synergistic Effects of MgO Passivation and CdS SILAR Cycle Optimization in TiO₂-Based Quantum Dot-Sensitized Solar CellsFigure 5. (a) Tauc plot of the TiO₂/CdS photoelectrode used to estimate the optical band gap. (b) Tauc plot of the MgO-passivated TiO₂/CdS photoelectrode4.3.3 Mott–Schottky Analysis (Flat-Band Potential & Carrier Density)The Mott–Schottky plot of the optimized TiO₂/MgO/CdS photoelectrode is shown in Figure 6. The positive slope of the C−2versus applied potential plot confirms the n-type semiconducting nature of the photoelectrode, which is typical for TiO₂-based materials (Peter, 2007). Extrapolation of the linear region yields a flat-band potential of approximately 0.58 V vs the reference electrode, indicating favorable band alignment for electron injection from CdS into TiO₂.Figure 6. Mott–Schottky (C−2vs. applied potential) plot of the TiO₂/MgO/CdS photoelectrode, showing a positive slope that confirms n-type semiconducting behavior.The relatively steep slope observed in the Mott–Schottky plot suggests a high donor density, which facilitates efficient charge transport and reduced recombination losses (Bisquert et al.,

Proceedings of the Technical Sessions, 42 (2026) 47-61Institute of Physics, Sri Lanka 57Synergistic Effects of MgO Passivation and CdS SILAR Cycle Optimization in TiO₂-Based Quantum Dot-Sensitized Solar Cells2009). The incorporation of MgO is expected to reduce surface trap density and enhance interfacial charge separation, in agreement with the improved IPCE and photocurrent performance of the optimized electrode.4.4 Structural and Chemical Characterization of Optimized Films4.4.1 XRD AnalysisFigure 7 shows the XRD patterns of TiO₂/CdS and TiO₂/MgO/CdS photoelectrodes. The diffraction peaks observed at characteristic 2θ positions correspond to the anatase phase of TiO₂, confirming that the crystal structure of TiO₂ remains intact after CdS sensitization and MgO modification (Bavykin et al., 2006). Weak reflections associated with CdS are also detected, indicating successful CdS deposition.Figure 7. X-ray diffraction (XRD) patterns of TiO₂/CdS and MgO-modified TiO₂/CdSphotoelectrodes, showing characteristic anatase TiO₂ reflectionsNo distinct MgO peaks are observed, suggesting that MgO is present either as an amorphous layer or in a highly dispersed form below the XRD detection limit, as commonly reported for ultrathin oxide passivation layers (Hodes & Cahen, 2014). This structural preservation is beneficial for maintaining efficient charge transport pathways within the photoelectrode.4.4.2 FTIR SpectroscopyFigure 8 presents the FTIR spectra of TiO₂/CdS and TiO₂/MgO/CdS photoelectrodes. Both samples exhibit a broad absorption band in the 3200–3600 cm⁻¹ region, corresponding to O–H stretching vibrations from surface hydroxyl groups, which are known to enhance interfacial charge transfer (Socrates, 2004). The TiO₂/MgO/CdS electrode shows a slight reduction in hydroxyl-related intensity, indicating improved surface passivation.

Proceedings of the Technical Sessions, 42 (2026) 47-61Institute of Physics, Sri Lanka 58Synergistic Effects of MgO Passivation and CdS SILAR Cycle Optimization in TiO₂-Based Quantum Dot-Sensitized Solar CellsAdditionally, metal–oxygen vibrational modes observed in the lower wavenumber region confirm the formation of Ti–O and Mg–O bonds, supporting the successful incorporation of MgO into the composite structure (Nair & Nair, 1991). These chemical modifications are consistent with reduced surface recombination and enhanced photoelectrochemical performance.Figure 8. FTIR spectra of TiO₂/CdS and MgO-passivated TiO₂/CdS photoelectrodes, illustrating characteristic vibrational modes of Ti–O and Cd–S bonds4.4.3 SEM MorphologyFigure 9 shows SEM images of TiO₂/CdS and TiO₂/MgO/CdS photoelectrodes. The TiO₂/CdS sample displays a densely packed nanoparticulate morphology with noticeable agglomeration, which can limit effective electrolyte penetration and charge transport (Grätzel, 2001). In contrast, the TiO₂/MgO/CdS electrode exhibits a more uniform and porous morphology with reduced particle aggregation.TiO2/CdS TiO2/MgO/CdSFigure 9. SEM micrographs of (a) TiO₂/CdS and (b) MgO-modified TiO₂/CdS photoelectrodes, showing a nanoparticulate morphology

Proceedings of the Technical Sessions, 42 (2026) 47-61Institute of Physics, Sri Lanka 59Synergistic Effects of MgO Passivation and CdS SILAR Cycle Optimization in TiO₂-Based Quantum Dot-Sensitized Solar CellsThe improved surface morphology enhances the effective surface area and facilitates better interfacial contact between the semiconductor and electrolyte, leading to improved charge transfer and reduced recombination losses (Zaban et al., 1998). These morphological advantages correlate well with the enhanced IPCE and electrochemical performance observed for the optimized photoelectrode.5. CONCLUSIONIn this work, the combined effects of MgO surface passivation and controlled CdS SILAR sensitization on TiO₂-based quantum dot–sensitized solar cells were systematically investigated through optical, electronic, structural, electrochemical, and photovoltaic analyses. The results clearly demonstrate that MgO (7%) modification and CdS loading jointly govern light harvesting, interfacial charge transport, and recombination behaviour in the photoanodes.Optical studies confirmed that CdS sensitization effectively extends the photo response into the visible region with an absorption onset around 520–540 nm, while MgO incorporation enhances absorption intensity and introduces a longer-wavelength tail extending toward 750–800 nm. Tauc analysis revealed band gaps of 2.066 eV for TiO₂/CdS and 2.109 eV for TiO₂/MgO/CdS, indicating that MgO does not act as a visible absorber but sharpens the absorption edge through surface passivation. Mott–Schottky analysis confirmed n-type behaviour and yielded a flat-band potential of approximately 0.58 V, verifying that favourableband alignment for electron injection and transport is preserved after MgO modification.Structural and morphological characterizations further supported these findings. XRD confirmed retention of the anatase TiO₂ framework after CdS deposition and MgO modification, while the absence of distinct MgO reflections suggests a highly dispersed or ultrathin passivation layer. FTIR spectra revealed modified surface chemistry, including hydroxyl-related features and metal–oxygen vibrational modes, consistent with reduced defectassisted recombination. SEM analysis showed a more uniform and less agglomerated morphology for TiO₂/MgO/CdS, favouring improved electrolyte penetration and interfacial contact.Electrochemical and photovoltaic measurements revealed a strong dependence on CdS SILAR cycle number. The optimized 12-cycle photoanode exhibited the best-balanced performance, delivering a maximum efficiency of 0.451% with Jsc = 3.091 mA cm⁻² and Voc = 451.8 mV, alongside the highest output power. Although higher CdS cycles produced slightly larger photocurrents, increased recombination and transport limitations reduced the overall efficiency. EIS and IPCE analyses confirmed that optimal device performance arises from a balance between light harvesting, charge-transfer resistance, and recombination kinetics rather than from maximizing any single parameter.Overall, this study identifies 12 CdS SILAR cycles as the optimal sensitization condition for TiO₂/MgO (7%) photoanodes and demonstrates that synergistic interface engineering is a viable pathway for enhancing the performance and stability of CdS-based QDSSCs.

Proceedings of the Technical Sessions, 42 (2026) 47-61Institute of Physics, Sri Lanka 60Synergistic Effects of MgO Passivation and CdS SILAR Cycle Optimization in TiO₂-Based Quantum Dot-Sensitized Solar Cells6. REFERENCES[1] Ahmed, S., Ren, F., Chen, X., & Zhao, Y. (2020). Surface passivation of TiO₂ with alkalineearth metal oxides for improved light harvesting in quantum dot–sensitized solar cells. Applied Surface Science, 504, 144426. https://doi.org/10.1016/j.apsusc.2019.144426[2] Bavykin, D. V., Friedrich, J. M., & Walsh, F. C. (2006). Protonated titanates and TiO₂ nanostructures. Advanced Materials, 18(21), 2807–2824. https://doi.org/10.1002/adma.200502696[3] Bisquert, J., Fabregat-Santiago, F., Mora-Seró, I., Garcia-Belmonte, G., & Giménez, S. (2009). Electron lifetime in dye-sensitized solar cells: Theory and interpretation of measurements. The Journal of Physical Chemistry C, 113(40), 17278–17290. https://doi.org/10.1021/jp902347r[4] Fabregat-Santiago, F., Mora-Seró, I., Garcia-Belmonte, G., & Bisquert, J. (2011). Cyclic voltammetry and impedance spectroscopy of dye-sensitized solar cells. The Journal of Physical Chemistry C, 115(18), 9250–9258. https://doi.org/10.1021/jp200239b[5] Grätzel, M. (2001). Photoelectrochemical cells. Nature, 414(6861), 338–344. https://doi.org/10.1038/35104607[6] Hagfeldt, A., Boschloo, G., Sun, L., Kloo, L., & Pettersson, H. (2010). Dye-sensitized solar cells. Chemical Reviews, 110(11), 6595–6663. https://doi.org/10.1021/cr900356p[7] Hodes, G. (2008). Comparison of dye- and semiconductor-sensitized porous nanocrystalline liquid junction solar cells. The Journal of Physical Chemistry C, 112(46), 17778–17787. https://doi.org/10.1021/jp806447h[8] Hodes, G., & Cahen, D. (2014). Photovoltaics: Between efficiency and stability. Nature Photonics, 8(2), 87–88. https://doi.org/10.1038/nphoton.2013.353[9] Huang, J., Wang, L., & Chen, H. (2018). Role of MgO interfacial layers in suppressing defect states and modifying band gaps in semiconductor heterostructures. Applied Physics A, 124(3), 213. https://doi.org/10.1007/s00339-018-1655-9[10] Kamat, P. V. (2013). Quantum dot solar cells: Semiconductor nanocrystals as light harvesters. The Journal of Physical Chemistry Letters, 4(6), 908–918. https://doi.org/10.1021/jz400052e[11] Karthikeyan, C., Tamilselvan, A., & Senthil, T. S. (2017). Electrochemical study of MgOmodified TiO₂ electrodes: Impact on flat-band potential and charge transfer resistance. Applied Surface Science, 399, 420–428. https://doi.org/10.1016/j.apsusc.2016.12.020[12] Liu, Z., Wu, Q., Zhang, T., & Dai, S. (2020). Interface engineering of CdS-sensitized TiO₂ photoanodes via MgO modification for improved optoelectronic properties. Journal of Alloys and Compounds, 825, 154054. https://doi.org/10.1016/j.jallcom.2020.154054[13] Mora-Seró, I., & Bisquert, J. (2010). Breakthroughs in the development of quantum dot sensitized solar cells. The Journal of Physical Chemistry Letters, 1(20), 3046–3052. https://doi.org/10.1021/jz1012442

Proceedings of the Technical Sessions, 42 (2026) 47-61Institute of Physics, Sri Lanka 61Synergistic Effects of MgO Passivation and CdS SILAR Cycle Optimization in TiO₂-Based Quantum Dot-Sensitized Solar Cells[14] Nair, P. K., & Nair, M. T. S. (1991). Chemical bath deposition of semiconductor thin films. Journal of Physics D: Applied Physics, 24(1), 83–87. https://doi.org/10.1088/0022-3727/24/1/014[15] Peter, L. M. (2007). Characterization and modeling of dye-sensitized solar cells. The Journal of Physical Chemistry C, 111(18), 6601–6612. https://doi.org/10.1021/jp068092g[16] Rao, G., Wang, Z., & Yang, F. (2020). Investigation of band alignment and recombination suppression in MgO-coated TiO₂ electrodes. Electrochimica Acta, 332, 135476. https://doi.org/10.1016/j.electacta.2019.135476[17] Socrates, G. (2004). Infrared and Raman characteristic group frequencies (3rd ed.). Wiley.[18] Tachan, Z., Shalom, M., Hod, I., Rühle, S., Tirosh, S., & Zaban, A. (2011). PbS as a highly catalytic counter electrode for polysulfide-based quantum dot solar cells. The Journal of Physical Chemistry C, 115(13), 6162–6166. https://doi.org/10.1021/jp111739h[19] Tauc, J., Grigorovici, R., & Vancu, A. (1966). Optical properties and electronic structure of amorphous germanium. Physica Status Solidi, 15(2), 627–637. https://doi.org/10.1002/pssb.19660150224[20] Wang, F., Li, Z., & Zhou, Y. (2020). Morphology tuning of MgO-coated TiO₂ nanostructures for enhanced optoelectronic performance. Materials Chemistry and Physics, 246, 122818. https://doi.org/10.1016/j.matchemphys.2020.122818[21] Zaban, A., Micic, O. I., Gregg, B. A., & Nozik, A. J. (1998). Photosensitization of nanocrystalline TiO₂ electrodes. Langmuir, 14(12), 3153–3156. https://doi.org/10.1021/la971381f[22] Zhang, Q., Guo, X., Huang, X., Huang, S., Li, D., Luo, Y., & Meng, Q. (2011). Highly efficient CdS/CdSe-sensitized solar cells controlled by the deposition order of quantum dots. Physical Chemistry Chemical Physics, 13(11), 4659–4667. https://doi.org/10.1039/C0CP02328A[23] Zhang, X., Liu, Y., Wang, H., & Li, J. (2019). Enhanced visible-light absorption and charge separation in MgO-modified TiO₂ photoanodes for quantum dot–sensitized solar cells. Solar Energy Materials and Solar Cells, 200, 109958. https://doi.org/10.1016/j.solmat.2019.109958

Proceedings of the Technical Sessions, 42 (2026) 62-70Institute of Physics, Sri Lanka 62Simulating Proton Synchrotron Acceleration and Proton–Proton Collision Dynamics in the CERN Accelerator Complex using Unity 3DSimulating Proton Synchrotron Acceleration and Proton–Proton Collision Dynamics in the CERN Accelerator Complex using Unity 3DD.M.C.M.K. Dissanayake1, N. Wickramage1, K.M. Liyanage1, K.A.S. Lakshan11Department of Physics, University of Ruhuna, Matara, Sri [email protected]. ABSTRACTThis study is focused on developing an interactive simulation interface based on the synchrotron chain of the CERN accelerator complex, focusing on the Proton Synchrotron Booster (PSB), the Proton Synchrotron (PS), the Super Proton Synchrotron (SPS), and the Large Hadron Collider (LHC) and proton-proton collisions in the context of the Compact Muon Solenoid (CMS) detector. The Lorentz force-based algorithm was implemented to simulate beam dynamics in the synchrotron stages, while proton-proton collisions and particle generation after the collisions were simulated, inspired by particle physics concepts like QuarkGluon Interactions, Quark-Antiquark pair production, and energy-momentum relations, enabling the generation of secondary particles while conserving energy and momentum. Synchrotron models were developed in three dimensions using Blender and integrated into Unity 3D. The presented interface provides a simplified phenomenological framework intended for interactive visualization and educational applications, rather than for highprecision event-by-event physical predictions, providing an educational tool for researchers and students.2. INTRODUCTIONElementary particles are the fundamental building blocks of matter and energy that cannot be divided further. The Standard Model (SM) can be considered the most successful theory that accurately predicts vast experimental results to date. The collision of these particles with each other can produce different known or unknown particles, depending on the energy. Scientists collide particles at high speeds inside particle accelerators to discover new particles, study dark matter-related issues, understand forces, and more. The 1911 Alpha scattering experiment supervised by Ernest Rutherford is the foundational work that provided a conceptual breakthrough for collision experiments [1]. Since then, numerous particle collision experiments have been conducted by scientists to reveal the truth about particles. James Chadwick’s beryllium bombardment experiments in 1932 led to the discovery of neutrons. The great milestone of particle collision experiments was created by the cosmotron (1952-1966), which was the first accelerator in the world that reached the GeV range. The discovery of the antiproton in 1954 by the Bevatron, the construction of the Large Electron Positron (LEP) collider (1989-2000), the discovery of the bottom quark in 1977, and the top quark in 1995 at Fermilab are some of the important milestones in particle collision experiments. ([2], [3]) The first proton-proton and proton-antiproton collisions happened in the Intersecting Storage Rings

Proceedings of the Technical Sessions, 42 (2026) 62-70Institute of Physics, Sri Lanka 63Simulating Proton Synchrotron Acceleration and Proton–Proton Collision Dynamics in the CERN Accelerator Complex using Unity 3D(ISR) (1971-1984) in CERN. Ever since, various proton-proton collision experiments have been conducted. At present, experiments on proton-proton collisions mainly happen in the Large Hadron Collider (LHC) at the CERN accelerator complex. The CERN accelerator complex is the most advanced machine that human beings have ever created. CERN consists of a network of accelerators that accelerate particles to very high energies. Each machine is designed to increase the energy of particles to reach the target energy at the collision. The particle beams are initiated as negative hydrogen ion beams, which are boosted up to 160 MeV through the Linac-4 and injected into the proton synchrotron booster (PSB) as protons after a removal process of two electrons from each ion between the Linac-4 and PSB. The protons are accelerated up to 2 GeV inside the PSB and injected into the proton synchrotron (PS). In the next machines, the PS and Super Proton Synchrotron (SPS), the protons are accelerated up to 26 GeV and 450 GeV, respectively. Then, protons are transferred into the two beam pipes of the LHC to reach the final beam energy. The particle collisions happen in the LHC at 4 detectors: Compact Muon Solenoid (CMS), A Large Ion Collider Experiment (ALICE), A Toroidal LHC ApparatuS (ATLAS), and the Large Hadron Collider beauty (LHCb).( [4]). Conducting particle collision experiments in the real world at the CERN Accelerator complex is a very expensive process, and only a selected number of people can gain hands-on experience with these experiments. Developing a user-friendly, interactive, and physics-based interface/game that can simulate particle collisions at the accelerator complex will allow more science enthusiasts to get a significant idea about fundamental particles and their behaviors, and how the CERN accelerator complex operates, instead of going through the time-consuming and hard traditional learning methods. Stated thus, this research is focused on simulating proton acceleration in synchrotrons and pp collision events in the context of the CMS detector. The approximate 3-D models of the above-mentioned machines were designed using Blender 3D. Since the actual machines have very sophisticated systems, which lead to complex mathematical calculations that take much time and computer power to simulate in a virtual platform, the approximate algorithms were implemented to simulate the workflow of each machine with assumptions.3. METHODOLOGYThis study focuses on the simulation framework developed for the CERN synchrotron accelerator chain and pp collision processes. The framework models the synchrotron stages of the accelerator complex, including the PSB, PS, SPS, and the LHC [4]. In addition to synchrotron beam acceleration, pp collisions and the associated particle generation processes at the CMS detector are simulated and analyzed. The overall interface builds upon earlier work on the simulation of the LINAC-4 stage, which represents the initial acceleration component of the CERN accelerator chain [5]. Extending this foundation, the present study concentrates on the numerical modeling and interactive simulation of synchrotron acceleration and collision phenomena. For each accelerator considered in this work, three-dimensional models were created using Blender, exported in FBX format, and integrated into the Unity 3D environment. Particle dynamics, beam control, and collision processes were implemented using custom numerical algorithms developed in C#.

Proceedings of the Technical Sessions, 42 (2026) 62-70Institute of Physics, Sri Lanka 64Simulating Proton Synchrotron Acceleration and Proton–Proton Collision Dynamics in the CERN Accelerator Complex using Unity 3D3.1 The simulation algorithm for synchrotronsThe simulation algorithm was designed in such a way that a Lorentz force is applied to the rigid-body representation of each particle once it enters the Synchrotron region. The total force acting on a particle is given by?⃗ = ??⃗⃗⃗⃗⃗ + ??⃗⃗⃗⃗⃗⃗ (1)where ??⃗⃗⃗⃗⃗ and ??⃗⃗⃗⃗⃗ represent the electric and magnetic force components, respectively. To maintain an approximately constant radius of the circular particle trajectory during acceleration, the magnetic field strength was dynamically adjusted. The magnetic field vector was defined as?⃗⃗ = ??̂ (2)where b is the user-controlled magnetic field magnitude, and ?̂ is a unit vector indicating the field direction. The direction of the unit vector ⃗u was determined using the right-hand rule, considering the particle charge, velocity, and the desired curvature of the particle trajectory. The magnetic force acting on the particle was calculated using??⃗⃗⃗⃗⃗ = ?(?⃗ × ?⃗⃗) (3)where q is the particle charge, and ?⃗ is the velocity of the particle. The electric force was implemented to act in the direction of particle motion in order to increase the kinetic energy of the particle. It is given by??⃗⃗⃗⃗⃗ = ???̂ (4)where E is the magnitude of the electric field, and ?̂ is a unit vector in the direction of the particle velocity. This implementation allows the particle to follow an approximately circular trajectory with a nearly constant radius while its velocity and energy increase.3.2 Approximate 3D models for synchrotronsThe approximate model of the PSB was developed to represent all four superimposed rings and their geometrical arrangement.Figure 1: Three-dimensional model and ring layout of the PS Booster used in the simulation. (a) PS Booster showing all four rings and (b) PS Booster ring layout.

Proceedings of the Technical Sessions, 42 (2026) 62-70Institute of Physics, Sri Lanka 65Simulating Proton Synchrotron Acceleration and Proton–Proton Collision Dynamics in the CERN Accelerator Complex using Unity 3DThe approximate model of PS was designed as shown in Figures 2a and 2b, including eight dipole magnets and an RF accelerating unit placed after each dipole magnet. Figure 2: Approximate three-dimensional model of the PS used in the simulation. (a) ProtonSynchrotron – top view and (b) Proton Synchrotron – wireframe view.The approximate SPS model includes sixteen dipole magnets, with an RF accelerating unit placed after each dipole magnet.Figure 3: Approximate three-dimensional model of the Super Proton Synchrotron used in the simulation. (a) SPS – top view and (b) SPS – wireframe view.The approximate LHC model was designed as shown in Figures 4a–4c, including two beam pipes intersecting at four interaction points.(a) LHC - Top view (b) LHC - Side view (c) LHC - Wireframe viewFigure 4: Approximate three-dimensional model of the LHC used in the simulation3.3 Proton–Proton CollisionsThe process of two proton beams colliding with each other at very high energies, up to14 TeV and close to the speed of light, inside the LHC at the CMS collision point, leads to a series of complex processes in which secondary particles are generated. In this work, proton–proton collisions are modeled using a simplified, phenomenological algorithm designed for

Proceedings of the Technical Sessions, 42 (2026) 62-70Institute of Physics, Sri Lanka 66Simulating Proton Synchrotron Acceleration and Proton–Proton Collision Dynamics in the CERN Accelerator Complex using Unity 3Dinteractive visualization and educational purposes, rather than precise event-by-event physical prediction.(i) Assigning partons and generating quark–antiquark pairs(ii) Generating hadrons, leptons, and mesons(iii) Assigning energy and momentum to particles.3.3.1 Assigning partons and generating quark–antiquark pairsThe algorithm was designed in such a way that the number of interactions was assigned at the beginning. The parton generation function was called repeatedly for a number of times equal to the number of interactions.A simplified parton-selection scheme was implemented using normalized heuristic weights inspired by parton distribution functions, rather than full momentum-dependent PDFs. Heavy quarks were excluded except where dynamically generated during interactions. [6]A random number between 0 and 1 was generated, and all partons were considered one at a time while accumulating their Relative weight. If the cumulative probability exceeded the generated random number, the last considered parton was selected, and the origin of the particle (which proton the parton originated from) was assigned randomly.Table 1: Relative weight of each parton being inside a protonParton Relative weightu 0.3g 0.45d 0.15?̅ 0.05?̅ 0.05A method was then implemented to generate bosons via quark and gluon interactions. In this process, partons from different protons interacted randomly and produced photons, Z0 , W+, and W -bosons under the following rules.• If at least one of the interacting partons was a gluon (g), only neutral bosons were permitted.• If the interacting partons formed a quark–antiquark pair of the same flavor (u?̅or d?̅), the dominant annihilation process,q?̅→ γ / Z0was applied.

Proceedings of the Technical Sessions, 42 (2026) 62-70Institute of Physics, Sri Lanka 67Simulating Proton Synchrotron Acceleration and Proton–Proton Collision Dynamics in the CERN Accelerator Complex using Unity 3D• If the interacting partons were a quark–antiquark pair of different flavors, W bosons were generated:u + ?̅→?+, d + ?̅ →?−• If the interacting partons were quark–quark or antiquark–antiquark pairs, only neutral bosons were allowed.Throughout the whole boson generating process, the natural boson was selected from γ, Z0 using the weighting γ: Z0 ≈ 3: 1.In this model, the bosons act as intermediate virtual particles produced by interactions among quarks, antiquarks, or gluons. Accordingly, the electric charge removed from the initial state was recorded based on the identity of the intermediate boson generated in the interaction.???????? = {+1, ??? ?+,−1, ??? ?−,0, ??? ? ?? ?0The generated bosons were used to generate quarks and antiquarks using the following algorithm. The intermediate bosons were decayed using ?+ → u +?̅, ?− → ?̅+ ?̅quark-antiquark pairs were created by natural bosons according to γ/ Z0 → u + ?̅ , γ/Z0 → d + ?̅Following all interactions, the ???????? total charge of the generated antiquark and quarks wascalculated and compared to each other, and events where these two coincide were accepted, and generated quarks and anti-quarks were used to generate hadrons and mesons; all others were discarded.3.3.2 Generating hadrons and leptons Seventy percent of the generated quarks were randomly paired with antiquarks to produce mesons, while the remaining thirty percent of quarks were used to randomly produce neutrons and protons, reflecting meson production as more statistically probable than producing threequark baryons, since it needs only two quarks.In addition, a method was implemented to generate lepton pairs, corresponding to approximately one lepton pair per one hundred generated hadrons, because strong interaction hadronization processes are dominant compared to electroweak interactions, which generate leptons.

Proceedings of the Technical Sessions, 42 (2026) 62-70Institute of Physics, Sri Lanka 68Simulating Proton Synchrotron Acceleration and Proton–Proton Collision Dynamics in the CERN Accelerator Complex using Unity 3D3.3.3 Assigning energy and momentum to particlesThe collision energy value was assigned at the start of the process. Energy and momentum were assigned to each particle using the following method. A random energy value calculated according to the mass of the particle was assigned to each particle, and the magnitude of the momentum was calculated using the energy–momentum relation. |?⃗⃗| = √?2 − ?2 (5)• |?⃗⃗|: Magnitude of the particle momentum• E: Energy of the particle• m: Mass of the particleNatural units (c = 1) were used throughout the simulation.The direction of the momentum was assigned randomly, and the momentum vector was generated by multiplying the direction vector by the momentum magnitude. After energy and momentum values were assigned to all particles, a method was implemented to check momentum conservation and to adjust the momentum of the final particle such that the total momentum of the system was conserved.hadrons, mesons, and leptons were generated using quarks,anti-quarks, and gluons in colliding protons using above described algorithm and spawned at the CMS interaction point in the game scene as follows.Figure 5: Proton–proton collision and particle generation at the CMS interaction point. (a) Colored view and (b) Wireframe view.4. RESULTS AND DISCUSSIONThe main aim of this study was to design an interactive interface to simulate beam acceleration and proton–proton collision processes within the CERN Synchrotron chain. The simulation model is limited to a pedagogical scope in which learners can examine how varying magnetic field strength affects the motion of the particle inside the synchrotron, allowing the user to control the magnetic field strength to maintain a constant curvature radius. RF acceleration is implemented in an idealized manner without modeling the detailed phase stability. At the CMS interaction point, particle generation and energy–momentum assignment were implemented using a simplified probabilistic framework inspired by fundamental concepts of Quantum Chromodynamics, including Parton Distribution Functions, Quark-Gluon Interactions, and

Proceedings of the Technical Sessions, 42 (2026) 62-70Institute of Physics, Sri Lanka 69Simulating Proton Synchrotron Acceleration and Proton–Proton Collision Dynamics in the CERN Accelerator Complex using Unity 3DQuark-Antiquark pair production and energy-momentum conservation. Also, several real machine features are not implemented, including detailed RF phase stability, synchrotron radiation losses, space charge effects, magnetic field nonlinearities, and engineering constraints. The velocity of the particle was scaled by a factor of 1: 106 for simplicity and to avoid excessive computational complexity during real-time simulation, while preserving the qualitative behavior of the acceleration process. In addition, a real-time velocity–time graph was incorporated into the interface to visualize particle velocity evolution during acceleration. The final simulation model of the accelerator complex was developed and assembled as illustrated in the following.Figure 6: Simulation model – Top View Figure 7: Simulation model – IsometricFigure 8: Gameplay including velocity–time graph5. CONCLUSIONThe simulation of particle motion in the Synchrotron chain and the particle collision at CMS was completed using Unity3D as the game engine. From the Challenges faced throughout the research and the obtained results, some key findings are obtained. In the case of PSB simulation, a non-uniform circular motion where the velocity of the particle increases while

Proceedings of the Technical Sessions, 42 (2026) 62-70Institute of Physics, Sri Lanka 70Simulating Proton Synchrotron Acceleration and Proton–Proton Collision Dynamics in the CERN Accelerator Complex using Unity 3Dkeeping the radius and the circular path was successfully simulated, showing that the varying magnetic field can keep the radius constant while increasing velocity. The particle generation algorithm was able to generate particles according to theoretical models, demonstrating an approximate way of particle formation in nature. Unity3D was well-suited and flexible in the development process and allowed the development of this interactive simulation without making technical failures or incompatibilities, which proposes Unity3D as a well-suited Game Engine for particle physics simulations. Future work could focus on several improvements aimed at further fine-tuning and optimizing the model. The visualization effects consist of more information about the process that needs to be included to enhance users’ understanding. Beamfocusing effects need to be added to the accelerator chain, other than accelerating to level up the simulation into a more realistic state. To further validate the simulation, it could be compared with the experimental data, other than the theoretical predictions.This simulation provides an interactive way of studying particle beam acceleration andcollisions compared to traditional learning approaches, with its cross-platform capabilities and interactive gameplay options. This interface/game can be considered a valuable and userfriendly research and educational tool for understanding and visualizing complex phenomena in particle physics.6. REFERENCES[1] Tretkoff, E., May, 1911: Rutherford and the Discovery of the Atomic Nucleus, APS News: This Month in Physics History, American Physical Society (May 1, 2006).[2] CERN, Large Electron–Positron Collider, CERN.Available from: https://home.cern/science/accelerators/large-electron-positron-collider.[3] Fermilab, Key Discoveries in Particle Physics, Fermilab.Available from: https://www.fnal.gov/pub/science/particle-physics/keydiscoveries.html.[4] CERN, The CERN Accelerator Complex, CERN.Available from: https://home.cern/science/accelerators/accelerator-complex.[5] Dissanayake, D.M.C.M.K., Wickramage, N., Liyanage, K.M., and Lakshan, K.A.S., Simulating negative hydrogen ion acceleration in LINAC-4 using Unity 3D, arXiv:2503.00304 [physics.acc-ph] (2025).[6] LHAPDF Collaboration, LHAPDF parton distribution function sets, Available from: https://lhapdfsets.web.cern.ch/current/.

Proceedings of the Technical Sessions, 42 (2026) 71-80Institute of Physics, Sri Lanka 71Analysis of Vertical Pitch Scaling and Channel Tapering Effects on the Program/Erase Characteristics of 3D NAND Flash MemoryAnalysis of Vertical Pitch Scaling and Channel Tapering Effects on the Program/Erase Characteristics of 3D NAND Flash MemoryHasni Vidusini Ranasinghe1,2, L. Samiru Sankalana1,2, S. H. R. T. Sooriyagoda2, Luckshitha Suriyasena Liyanage1,21Nanoscience & Microelectronics Research Laboratory, University of Colombo, Sri Lanka2Department of Physics, University of Colombo, Sri [email protected]. ABSTRACTAt every new technology node, 3D NAND flash memory undergoes aggressive scaling to achieve higher bit density through vertical stacking of cells in the z-direction. Continuous vertical pitch scaling, however, heavily impacts memory cell performance, alongside geometric variations introduced by high-aspect-ratio etching, particularly channel tapering. This work independently investigates the impact of Z-scaling and channel tapering on the electrical characteristics of 3D NAND strings using Synopsys Sentaurus TCAD. To decouple these effects, Z-scaled structures with ideal vertical channels were simulated to evaluate pitch reduction, while tapered channel geometries were analyzed separately. The results reveal a clear operational asymmetry under Zscaling: programming efficiency severely degrades due to weakened gate-to-channel coupling and increased electrostatic interference, whereas erase efficiency improves as stronger vertical fringing fields enhance hole injection. Furthermore, channel tapering (evaluated from 0° to 1°) was found to inherently improve both program and erase efficiency through geometric field concentration at the tunnel oxide. These findings provide physical insight into the geometry-induced performance trade-offs in advanced 3D NAND devices.2. INTRODUCTION3D NAND flash memory has rapidly become the mainstream non-volatile storage technology, replacing planar NAND by leveraging vertical stacking, charge-trap cells, and increased bits-per-cell (TLC/QLC) to realize more than an order-of-magnitude areal-density gain exceeding 10 Gb/mm² at 176 layers [1] [2]. Density scaling is driven by wordline stacking, XYZ physical scaling, logical scaling from MLC (2 bits-per-cell) to TLC/QLC (3 or 4 bits-per-cell), and architectural advances such as CMOS-under-array and gate-all-around strings [1] [2]. As the roadmap pushes toward several hundred layers, maintaining uniform program/erase behavior, acceptable cell-to-cell interference, and stable threshold-voltage (Vth) distributions along ultra-tall strings has become a central challenge [1] [2].To further optimize bit cost, manufacturers are increasingly adopting vertical pitch scaling (Zscaling). By reducing the gate length and inter-layer dielectric thickness, this approach allows for a higher density of wordlines within a fixed vertical volume, thereby increasing capacity without

Proceedings of the Technical Sessions, 42 (2026) 71-80Institute of Physics, Sri Lanka 72Analysis of Vertical Pitch Scaling and Channel Tapering Effects on the Program/Erase Characteristics of 3D NAND Flash Memorya proportional increase in total stack height [2] [6] [7] [8]. However, stronger capacitive coupling and field crowding along the string make scaled cells more sensitive to program-inducedinterference, Vth shifts, and reliability margins, especially in TLC/QLC operation where the usable memory window is narrow [2] [6] [8]. While several studies have examined XYZ and ON‑pitch scaling, this work specifically isolates the impact of pure z‑pitch reduction to systematically quantify the resulting Vth and cell current (Icell) profiles along the stack [1] [2].However, the implementation of ultra-tall stacks is constrained by the physical limits of HighAspect-Ratio (HAR) etching. This HAR etching process in NAND memory fabrication inevitably leads to channel tapering and significant variations in critical dimensions (CD) between the top and bottom of the string [9] [10]. These geometric distortions constrict the effective width of the polysilicon channel, resulting in increased series resistance and degraded on-current (Ion). Consequently, the gradient in channel-hole size along the pillar widens the threshold voltage (Vth) distribution, severely compromising vertical cell-to-cell uniformity [3] [9] [10]. Furthermore, both TCAD simulations and experimental data indicate that the electrical uniformity of the string is highly sensitive to the structural profile. Specifically, the channel taper angle and the stack height have been shown to significantly alter the Vth distribution and transconductance characteristics across the vertical array [4]. While detailed analyses suggest that channel doping can be engineered to mitigate taper-induced non-uniformity [5] [9] [10], relying on doping adjustments adds process complexity. To understand the intrinsic geometric limits of the device, this study isolates the structural parameters, maintaining a constant doping profiles to specifically quantify the electrical degradation caused by Z-scaling and channel tapering alone. Furthermore, existing work generally treats z-pitch scaling and string tapering as intertwined integration outcomes rather than independently tunable design parameters. Many studies focus on density, program/erase schemes, and interference in scaled 3D NAND while assuming an ideal vertical channel, whereas others investigate tapering, etch angle, and doping optimization without systematically varying vertical pitch. As a result, the individual contributions of z-direction pitch scaling and HAR-induced taper to Vth and Icell non-uniformity along the string remain insufficiently quantified, leaving open how each parameter should be optimized to balance density, performance, and reliability in future high-stack 3D NAND.3. METHODOLOGY3.1. Simulation FrameworkThe analysis of 3D NAND flash memory characteristics was conducted using the Synopsys Sentaurus™ Technology Computer-Aided Design (TCAD) tools. The simulation flow involved Sentaurus Structure Editor (SDE) for parametric 2D device construction and meshing, and Sentaurus Device (S-Device) for the simulation of electrical characteristics and charge transport

Proceedings of the Technical Sessions, 42 (2026) 71-80Institute of Physics, Sri Lanka 73Analysis of Vertical Pitch Scaling and Channel Tapering Effects on the Program/Erase Characteristics of 3D NAND Flash Memoryphysics. To maintain computational efficiency while capturing the essential physics of the vertical string, a 2D cylindrical symmetry approach was employed. This allows the simulation of a 3D Macaroni-channel structure by rotating a 2D cross-section around the central axis, significantly reducing simulation time compared to full 3D structures without compromising accuracy for vertical bit-line analysis.3.2. 3D NAND Device StructureThe simulated baseline device in this study consists of a parameterized vertical NAND string with 5 wordlines (WL) (Figure 1a) to capture cell-to-cell interference and boundary effects of a typical memory cell. It utilizes a \"Macaroni\" channel architecture (Figure 1b), where a hollow cylindrical channel is filled with a dielectric filler to improve electrostatic control. The memory stack employs a SONOS (Silicon-Oxide-Nitride-Oxide-Silicon) configuration comprising a thin tunnel oxide for carrier injection, a charge trap nitride (CTN) layer for charge storage, a blocking oxide to prevent charge leakage, and a TiN control gate.3.3 Experimental Design and Geometrical VariationsTo decouple the effects of vertical scaling and process variations, the study was divided into two distinct simulation phases.(a) (b)Figure 1: (a) Vertical Simulation Domain: The 2D cylindrical simulation structure representing the vertical NAND string. It highlights the five active Wordlines (WL1–WL5) positioned between the Drain Select Gate (DSG) and Source Select Gate (SSG) along the Polysilicon channel. (b) Horizontal Cross-Section: Top-down view of the Macaroni channel architecture. The structure consists of concentric layers (from center to edge): Dielectric Filler, Polysilicon Channel, Tunnel Oxide, Charge Trap Nitride (CTN), Blocking Oxide, and the Control Gate.

Proceedings of the Technical Sessions, 42 (2026) 71-80Institute of Physics, Sri Lanka 74Analysis of Vertical Pitch Scaling and Channel Tapering Effects on the Program/Erase Characteristics of 3D NAND Flash Memory3.3.1 Phase I: Vertical Pitch Scaling (Z-Scaling)In the first phase, the effect of vertical density scaling was investigated using an idealized NAND string with a perfectly vertical channel. Scaling from less-scaled to more aggressively scaled geometries was achieved by simultaneously reducing the gate length (Lg) and inter-gate spacing while maintaining a constant gate-to-space ratio, thereby isolating the impact of vertical pitch reduction. The vertical pitch was progressively reduced from 70 nm (fromLg = 40 nm, Space = 30 nm) in the baseline structure to 35 nm in the most aggressively scaled node, with intermediate pitches of 63, 56, 49, and 42 nm. In other words, at each node the gate and space dimensions were reduced by 10%, corresponding to 4 nm reduction in gate length and 3 nm reduction in spacing (total 7 nm per node in vertical dimension).3.3.2 Phase II: Channel Tapering AnalysisIn the second phase, the impact of high-aspect-ratio (HAR) etching was investigated by introducing channel tapering, as illustrated in the cross-sectional schematic of the simulated 3D NAND structure (Figure 2). The vertical pitch was fixed at the baseline dimensions (Lg = 40 nm, Space = 30 nm) to isolate taper-induced effects from vertical scaling. The channel taper angle (?) was systematically increased from the ideal vertical HAR etch of ?∘to ?∘in ?. ?∘increments, representing realistic etch-induced deviations in tall memory stacks.To evaluate position-dependent non-uniformity along the vertical string, electrical characteristics were extracted at three representative wordlines. WL5, near the top of the stack, exhibits the largest effective channel diameter, while WL1 at the bottom experiences the strongest channel constriction due to tapering. WL3, located at the mid-height of the string, serves as an intermediate reference between these two extremes.4. SIMULATION RESULTS AND DISCUSSION4.1. Impact of Vertical Pitch (Z-Scaling) on Program/Erase CharacteristicsThe influence of vertical density scaling on the program characteristics of the 3D NAND string was evaluated by analyzing the threshold voltage (Vth) shifts across six geometric nodes, ranging from the baseline (Lg = 40 nm, Space=30 nm) to an aggressively scaled node (Lg = 20 nm, Space=15 nm).Figure 2: Channel Tapering Model. Cross-sectional illustration of the simulated NAND string featuring a HighAspect-Ratio (HAR) etch profile. The diagram highlights the Taper Angle (θ), defined as the sidewall deviation from the ideal vertical axis (0°).

Proceedings of the Technical Sessions, 42 (2026) 71-80Institute of Physics, Sri Lanka 75Analysis of Vertical Pitch Scaling and Channel Tapering Effects on the Program/Erase Characteristics of 3D NAND Flash MemoryFigure 3 presents the ISPP (Incremental Step Pulse Program) characteristics extracted using both the Constant Current (Vth at Id = 1 μA) and Maximum Transconductance (Vth at max gm) methods. A distinct, monotonic degradation in the threshold voltage is observed as the vertical pitch is reduced. As shown in the simulation results, for any fixed program voltage (Vpgm), the Vth decreases significantly as the device dimensions shrink. For instance, at a programming bias of 20 V, the baseline node exhibits a Vth of 6.868 V, whereas at 20 nm node the Vth drops to 3.178 V. This parallel downward shift in the programming window is primarily governed by short-channel effects (SCE) and proximity interactions. As the gate length (Lg) shortens, the control gate loses electrostatic authority over the channel potential, leading to a natural rollback in the baseline Vth. Simultaneously, the reduction in inter-gate spacing enhances the impact of the fringing fields or electrostatic coupling from the neighboring cells. As illustrated in Figure 3, the ISPP curves exhibit a systematic downward and rightward shift as the vertical pitch is aggressively scaled. For instance, to achieve a target threshold voltage (Vth) of 4.0 V, the reference node (Lg = 40 nm, Space = 30 nm) requires a program voltage (Vpgm) of approximately 16.5 V. In contrast, the most scaled node (Lg = 20 nm, Space = 15 nm) requires a significantly higher Vpgm of nearly 21.0 V to reach the exact same Vth level. This rightward shift is a direct effect of degraded programming efficiency. The reduced inter-gate spacing enhances electrostatic coupling and fringing fields from adjacent wordlines, which disperses the localized electric field intended for the target cell. Consequently, charge localization within the charge trap nitride (CTN) layer is disrupted, requiring higher program voltages to force the equivalent Vth shift compared to larger technology nodes.(a) (b)Figure 3: Impact of Z-scaling on ISPP characteristics extracted via (a) Constant Current (Id = 1 μA)and (b) Maximum Transconductance methods.

Proceedings of the Technical Sessions, 42 (2026) 71-80Institute of Physics, Sri Lanka 76Analysis of Vertical Pitch Scaling and Channel Tapering Effects on the Program/Erase Characteristics of 3D NAND Flash MemoryTo provide physical insight into the mechanisms driving the observed programming trends, Figure 4 compares the simulated absolute electric field profiles for the reference (Lg = 40 nm) and ultra-scaled (Lg = 20 nm) geometries at a program bias of Vpgm = 20 V. For the reference node (Figure 4a), a high-intensity residual electric field (indicated by red/yellow contours) is observed directly beneath the target control gate (WL3) due to a high concentration of electrons successfully trapped in the CTN layer after the 20 V programming pulse. This high trapped charge density directly corresponds to the significant Vth shift observed in the ISPP characteristics.In contrast, the ultra-scaled node (Figure 4b) reveals a significantly weaker residual electric field (green/light-blue contours) after experiencing the exact same 20 V programming pulse. This reduction in field intensity visually demonstrates that substantially fewer charges were trapped in the nitride layer. The aggressive Z-scaling, which reduces the gate length and inter-gate spacing, degrades the programming efficiency due to a reduced gate coupling area and increased parasitic interference from adjacent cells during the programming operation. As a result, the process of injecting and storing electrons is severely weakened. Figure 4b clearly displays this loss of trapped charges. This matches the degraded performance shown in the ISPP graphs and explains why ultrascaled devices need much higher program voltages to reach the same target Vth.Interestingly, the Erase characteristics, illustrated in Figure 5, reveal that Z-scaling introduces a significant operational asymmetry. During the Incremental Step Pulse Erase (ISPE) operation, the aggressively scaled geometries consistently exhibit lower (more negative) threshold voltages for a given erase bias (Vers). While Z-scaling degrades programming efficiency due to weakened gateto-channel coupling and increased charge interference, it paradoxically improves erase efficiency. As the wordline spacing shrinks, stronger vertical fringing fields emerge. These fields increase the (a) (b)Figure 4: Residual Electric Field Evolution with Z-Scaling. Comparison of the absolute electric field generated by trapped charges after a 20 V programming pulse for (a) the reference node (Lg =40 nm) and (b) the ultra-scaled node (Lg = 20 nm). The weaker field intensity in the ultra-scaled node highlights the reduced charge-trapping efficiency associated with aggressive Z-scaling. The provided colour scale for the absolute electric field applies to both structures.WL3WL3

Proceedings of the Technical Sessions, 42 (2026) 71-80Institute of Physics, Sri Lanka 77Analysis of Vertical Pitch Scaling and Channel Tapering Effects on the Program/Erase Characteristics of 3D NAND Flash Memoryeffective electric field across the tunnel oxide under erase conditions, making hole injectionsignificantly more efficient.The aggressive scaling amplifies the vertical fringing fields, which penetrate the channel more effectively due to the reduced inter-gate spacing. While this facilitates deep erase saturation (as seen in the 20 nm node reaching below -4 V at Vers = 24 V), it introduces a significant operational asymmetry. The consistent outcomes between the Constant Current and Max gm extraction techniques confirm that this behavior is intrinsic to the geometric scaling rather than an artifact of the measurement method.4.2 Impact of Channel Tapering on Device PerformanceThe second phase of the study investigated the impact of the channel etch profile, simulating thetransition from an ideal vertical string (? = 0∘) to a realistic tapered profile (? = 1∘) that usually results from non-ideal HAR etch process. As the taper angle increases, the channel diameter reduces from the drain side (top) to the source side (bottom), introducing a \"curvature effect\". The reduced radius of curvature in the tapered layers enhances the electric field across the tunnel oxide for a given gate bias, directly influencing carrier injection efficiency.The simulation results confirm that this field enhancement accelerates both program and eraseoperations. Figure 6a illustrates the Program Characteristics, where the 1∘tapered string exhibits a consistently higher threshold voltage (Vth) compared to the vertical baseline. For instance, at a programming voltage of Vpgm = 20 V, the Vth shifts from 6.868 V (? = 0∘ vertical) to 7.642 V (?= 1∘).(a) (b)Figure 5: Impact of Z-scaling on ISPE characteristics extracted via (a) Constant Current (Id = 1 μA) and (b) Maximum Transconductance methods.

Proceedings of the Technical Sessions, 42 (2026) 71-80Institute of Physics, Sri Lanka 78Analysis of Vertical Pitch Scaling and Channel Tapering Effects on the Program/Erase Characteristics of 3D NAND Flash MemoryA symmetric trend is observed in the Erase Characteristics (Figure 6b). The enhanced electric field facilitates hole injection, allowing the tapered device to erase deeper. At an erase voltage of Vers = 24 V, the Vth drops to -3.615 V for the 1∘structure, compared to -2.309 V for the baseline. These findings indicate that geometric tapering concentrates the local electric field, which naturally enhances the efficiency of the program and erase operations for the same wordline.5. CONCLUSIONThis study investigated the impact of vertical density scaling (Z-scaling) and channel tapering on 3D NAND Flash performance using TCAD simulations. The results demonstrate a distinct operational asymmetry under Z-scaling. While aggressive pitch reduction severely degrades the programming threshold voltage (Vth) due to weakened gate coupling and enhanced electrostatic interference, it paradoxically improves erase efficiency as stronger vertical fringing fields enhance hole injection. Conversely, channel tapering (evaluated from 0° to 1°) was found to improve program and erase efficiency through geometric field enhancement.Ultimately, this work establishes that advancing 3D NAND architectures requires navigating distinct structural trade-offs. While the physical curvature of channel tapering provides an unintended benefit to carrier injection efficiency, the programming of aggressive Z-scaled nodes is significantly hindered by severe electrostatic interference. To fully realize the density benefits of high-aspect-ratio 3D NAND, future device designs must prioritize minimizing this enhanced cell-to-cell coupling in ultra-scaled pitches, potentially through the adoption of advanced programming algorithms or improved isolation techniques.(a) (b)Figure 6: Program (left) and erase (right) threshold voltage (?th) characteristics of the middle wordline (WL3) as a function of applied program and erase voltages for different channel taper angles. The curves highlight the influence of taper-induced channel geometry variations on programming and erase behavior, with threshold voltage extracted at ?cell = 1 ?A.

Proceedings of the Technical Sessions, 42 (2026) 71-80Institute of Physics, Sri Lanka 79Analysis of Vertical Pitch Scaling and Channel Tapering Effects on the Program/Erase Characteristics of 3D NAND Flash Memory6. ACKNOWLEDGMENTThe authors would like to express their sincere gratitude to Farazy Fahmy, Dr. Achintha Kondarage at Synopsys Sri Lanka and Dr. Sankalp Singh in Synopsys India, for providing access to the Sentaurus TCAD Educational License. Their assistance and guidance were essential for carrying out the device simulations in this work.7. REFERENCES[1] Goda, A. Recent Progress on 3D NAND Flash Technologies. Electronics. 2021 https://doi.org/10.3390/electronics10243156.[2] Goda, A. 3-D NAND Technology Achievements and Future Scaling Perspectives. IEEE Transactions on Electron Devices. 2020 https://doi.org/10.1109/TED.2020.2968079.[3] Oh, Y., Kim, K., Shin, S., Sim, H., Toan, N., Ono, T., & Song, Y. Impact of etch angles on cell characteristics in 3D NAND flash memory. Microelectron. J.. 2018 https://doi.org/10.1016/j.mejo.2018.06.009.[4] Kim, K., An, S., Jung, H., Yoo, K., & Kim, T. The Effects of Taper-Angle on the Electrical Characteristics of Vertical NAND Flash Memories. IEEE Electron Device Letters. 2017 https://doi.org/10.1109/LED.2017.2747631.[5] Bhatt, U., Kumar, A., Pakala, M., & Manhas, S. Effect of String Tapering on Threshold Voltage Distribution and Its Mitigation in a Vertical Channel 3D NAND Flash Memory. Extended Abstracts of the 2018 International Conference on Solid State Devices and Materials. 2018[6] Verreck, D., Arreghini, A., Bosch, G., Furnémont, A., & Rosmeulen, M. Program charge interference and mitigation in vertically scaled single and multiple-channel 3D NAND flash memory. 2021 International Conference on Simulation of Semiconductor Processes and Devices (SISPAD). 2021 https://doi.org/10.1109/sispad54002.2021.9592552.[7] Bae, H., Hong, S., & Park, J. Systematic Analysis of Spacer and Gate Length Scaling on Memory Characteristics in 3D NAND Flash Memory. Applied Sciences. 2024 https://doi.org/10.3390/app14156689.[8] Verreck, D., Arreghini, A., Schanovsky, F., Rzepa, G., Stanojevic, Z., Mitterbauer, F., Kernstock, C., Baumgartner, O., Karner, M., Van Den Bosch, G., & Rosmeulen, M. Understanding the ISPP Slope in Charge Trap Flash Memory and its Impact on 3-D NAND Scaling. 2021 IEEEInternational Electron Devices Meeting (IEDM). 2021 https://doi.org/10.1109/iedm19574.2021.9720506.

Proceedings of the Technical Sessions, 42 (2026) 71-80Institute of Physics, Sri Lanka 80Analysis of Vertical Pitch Scaling and Channel Tapering Effects on the Program/Erase Characteristics of 3D NAND Flash Memory[9] Lee, J., Jung, W., Park, J., Yoo, K., & Kim, T. Effect of the Blocking Oxide Layer With Asymmetric Taper Angles in 3-D NAND Flash Memories. IEEE Journal of the Electron Devices Society. 2021; 9. https://doi.org/10.1109/jeds.2021.3104843.[10] Bhatt, U., Manhas, S., Kumar, A., Pakala, M., & Yieh, E. Mitigating the Impact of Channel Tapering in Vertical Channel 3-D NAND. IEEE Transactions on Electron Devices. 2020; 67. https://doi.org/10.1109/ted.2020.2967869.[11] Choi, Y., Hong, S., & Park, J. Innovative Programming Approaches to Address Z-Interference in High-Density 3D NAND Flash Memory. Electronics. 2024 https://doi.org/10.3390/electronics13163123.

Proceedings of the Technical Sessions, 42 (2026) 81-89Institute of Physics, Sri Lanka 81End-to-End Simulation of Muon Scattering TomographyEnd-to-End Simulation of Muon Scattering TomographyM.D.S.N.Yasodaraa*, R.M.I.D. Gamage a, M. Lagrange b, K.M. Liyanagea, N.M. Wickramage aaDepartment of Physics, University of Ruhuna, Matara, Sri Lanka, bUniversité Catholique de Louvain, [email protected]. ABSTRACTMuography utilizes cosmic muons to investigate internal structures via Muon Absorption (MA) and Muon Scattering Tomography (MST). This study focuses on MST to estimate relative density and identify internal materials. Trajectories were reconstructed using the Point of Closest Approach (POCA) method and analyzed via 2-D and 3-D visualizations. Gaussian smoothing was applied to enhance image quality, with sigma (σ) values between 1 and 2 identified as optimal for balancing noise suppression and detail preservation. Keywords: Muon Scattering Tomography, Cosmic muons, Point of Closest Approach (POCA), Relative density estimation, Gaussian smoothing, Gaussian kernel size.2. INTRODUCTIONHigh-energy cosmic rays, primarily protons, or atomic nuclei reach the Earth’s atmosphere and interact with atmospheric nuclei. These collisions produce a cascade of secondary particles, including pions, muons, electrons, neutrinos, and photons, which spread out as they move downward. This is called a cosmic shower. The majority of the Earth’s atmosphere is contained within 16 km and most atmospheric muons are produced at a height of around 15 km. Muons (µ) are elementary particles in the lepton family with the same electric charge and spin as the electron. It has a mass that is approximately 200 times greater than that of the electron and travels at relativistic speed close to the speed of light. Muon was discovered as the result of the research of cosmic sea level radiation in 1937. At the sea level, majority of charged particles around 80% are muons, arriving at a frequency of approximately 100 Hz. m−2[1]. Due to their high energies, approaching relativistic speeds, and primarily weak interactions with matter, cosmic muons exhibit a high degree of penetration. Muons have a mean lifetime of approximately τ ≈ 2.2µs before decaying into electrons and neutrinos. This lifetime is longer than that of most other secondary cosmic-ray particles. A muon is a negatively charged particle. The corresponding antiparticle is the positive muon, denoted as µ+. As shown in Figure 1, the muon flux at ground level is substantially higher than the flux of electrons and positrons [1].Muons have a high penetrating power and can travel into Earth’s surface, often reaching depths of more than 1000 meters. Their penetration range increases with energy. They have the greatest penetration capacity even in dense materials such as lead and uranium [2], compared to other particles like electrons or protons, which interact more strongly with matter. These

Proceedings of the Technical Sessions, 42 (2026) 81-89Institute of Physics, Sri Lanka 82End-to-End Simulation of Muon Scattering Tomographycharacteristics of muons are suitable for examining the interiors of large objects,including volcanoes and pyramids, etc. As muons travel through a medium, muons canexperience Multiple Coulomb Scattering (MCS) from atomic nuclei. as shown in the Figure 2.As atomic nuclei are much heavier than muons, even small-angle scatterings can significantly deflect the muon from its original path. This happens because muons, like any other charged particle, are deflected from their initial trajectory when they interact electromagnetically with the nuclei of materials. This phenomenon is accurately described by Rutherford’s formula for the single scattering cross section [3]. The energy loss of muons forms the basis for muon absorption radiography [4], while as shown in the Figure 4, the angular deflection of muons is the foundation for muon scattering tomography (MST). Therefore, MST is a unique muography technique that utilizes a multiple-coulomb scattering mechanism. When a muon traverses a material with a high Z value (as shown in the Figure 3) [5], it experiences a larger deviation due to increased scattering. Conversely, materials with lower Z values result in smaller scattering angles. One of the main advantages of using this method is that muons can penetrate objects without causing destruction. The path of the muon passing through the detector and the investigated object is determined by the specific points where it makes a hit with the detector, allowing the trajectory of the muon to be traced. This research project includes analyzing the process between the hit Figure 2: A schematic view of multiple coulomb scattering process of the muon [9]Figure 1: Vertical fluxes of various particles, for momenta larger than 1GeV [3]Figure 3: The conventional design of amuon tomography system [12]Figure 4: Muon scatter with different materials [11]

Proceedings of the Technical Sessions, 42 (2026) 81-89Institute of Physics, Sri Lanka 83End-to-End Simulation of Muon Scattering Tomographypoints of the detector planes and the internal reconstruction of the object under investigation. The technique has significant potential in various fields, including national security [5], industrial inspection [6] and scientific research. Specifically, MST is completely passive, exploiting naturally occurring secondary cosmic radiation, and is therefore safe for humans and animals.3. MATERIALS AND METHODOLOGYSimulation data allow researchers to evaluate performance, optimize algorithms, and interpret results before applying the technology to real-world scenarios. The CRY software generates realistic cosmic-ray particle data, which are used as input for GEANT4 simulations ([7], [8]. GEANT4 is then utilized to simulate the detector response and energy deposition.In real-life applications, scintillator-based detectors [8] or gaseous detectors [9] are used. These high-energy muons naturally travel along nearly straight path, as they experience minimal interaction with low-density materials or free space (like air). Also, muon tracks are straight lines in the absence of a magnetic field. Therefore, any deflection of their trajectory occurs only due to their interaction with high-density materials. However, when muons traverse detector planes (shown in Figure 5), the recorded hit positions are affected by spatial resolution errors arising from detector limitations, calibration uncertainties, and environmental factors. Therefore, the linear least squares method determines the best-fitting straight line (?(?) =? × ? + ?) through a set of points. The direction vector of the fitted line is given by A, and the coordinates of a point on the fitted line are given by B. Then, the muon track can be reconstructed. To get the muon angle distribution, zenith and azimuthal angles were calculated[10].The zenith angle (θ) describes the direction of incoming cosmic muons relative to the detector's orientation, providing the necessary information for trajectory analysis. The range of θ is 0◦ ≤ θ ≤ 90◦. where, θ = 0◦corresponds to vertically incident muons propagating along the negative z-direction, and θ = 90◦for horizontal muons. The zenith angle θ is computed as:? = cos−1 (?√?2 + ?2 + ?2)Figure 5: Muon’s path through the detector and object [10](1)(2.1)

Proceedings of the Technical Sessions, 42 (2026) 81-89Institute of Physics, Sri Lanka 84End-to-End Simulation of Muon Scattering TomographyThe coordinates (x, y, z) represent a point on the fitted muon trajectory. The azimuthal angle (ϕ), defined as the angle between the track's projection onto the xy - plane and the x-axis, exhibits a uniform distribution due to the isotropic arrival of cosmic-ray muons.? = ???(?) cos−1 (?√?2+?2) ; ???(?) = {1 ?? ? > 00 ?? ? = 0−1 ?? ? < 0The zenith angles of the incoming and outgoing tracks are computed as in, ??? and ????, and the azimuthal angles of the incoming and outgoing tracks are computed as ??? and ????. The scattering angle is defined as the angle between the direction (∆?) of the incoming and outgoing muon. ∆? = cos−1(?⃗⃗⃗⃗??⃗⃗ ∙????⃗⃗⃗⃗⃗⃗⃗⃗⃗ |?⃗⃗⃗⃗??⃗⃗ ||????⃗⃗⃗⃗⃗⃗⃗⃗⃗ |)? ?? and ? ??? represent incoming and outgoing direction vectors, respectively. Events with nearly parallel incoming and outgoing tracks were rejected by removing tracks with minimal angular deviation. The tracking summary was obtained after selecting non-parallel angles, which showed the distribution between the number of events with zenith and azimuthal angles. The distribution of angles was able to provide a comprehension of the shape of the object from which the data was obtained. Masking is a powerful tool that allows us to filter and select the data for specific conditions efficiently. It simplifies large data sets to be useful for analysis and improves performance. Here, the mask is utilized to apply the scattering angle cutoff (∆? >∆? ???; ∆? ??? = 0.001) and to isolate non-parallel events (when the theta angle was close to zero, its tracks were considered to be parallel to each other, because their direction vectors were nearly aligned).3.1 POCA reconstruction algorithmIn particle tracking, the Point of Closest Approach (POCA) between two trajectories is essential in applications such as muon scattering tomography and image reconstruction. The POCA algorithm is a fundamental tool in various domains such as particle tracking and tomography. It was assumed that the muon was scattered only once at the point of closest approach between the incoming and outgoing tracks.3.2 POCA points computationAs shown in Figure 6, Q1 and Q2 were the points of the incoming track L1 and outgoing track L2, respectively. Then, the POCA location M is defined as,? =(?2−?1)2+ ?1The POCA point is shown as a blue point in Figure 7. This plot illustrates a single POCA point. To ensure a focused analysis, a specific Volume of Interest (VOI) was defined to analyze and reconstruct these POCA points. (4)(2.4)(2)(2.2)(3)(2.3)

Proceedings of the Technical Sessions, 42 (2026) 81-89Institute of Physics, Sri Lanka 85End-to-End Simulation of Muon Scattering TomographyThe VOI was represented as the spatial region of analysis that should be focused. It ensures that only points within the specified volume are considered. Within this area of interest, the scattering behavior is clearly interpreted. The dimensions and voxel size of the VOI were selected based on the characteristics of the interaction region to optimize resolution and accuracy. In this study, the calculated POCA points are located within the specified spatial boundaries of the VOI. The multiple-projection of POCA points distribution was visualized in the 2-D histogram and was visualized as a three-dimensional point cloud. 3.3 Optimal choice of Gaussian smoothing kernelA Gaussian smoothing filter is used in image processing to reduce noise, smooth details, and preserve important features. The filter’s core is based on the Gaussian function (?(?, ?, ?)), which creates a weighted average of surrounding values, giving more importance to nearby points and less importance to distant points. In 2-D (for images), it’s defined as:?(?, ?, ?) =12??2?−?2+?2+?22?2Here, x, y and z are spatial coordinates. σ is the standard deviation, controlling the width of the Gaussian curve and the amount of smoothing. The kernel size is defined by the standard deviation (σ) in each direction. By changing the values of ?? and ?? the number of smoothing changes, which directly impacts the level of detail retained in the data. The standard deviation value in z - dimension (??) was kept steady. The standard deviation σ of the Gaussian determines the amount of smoothing. For instance, a small standard deviation value such as σ = 1 results in a narrow, peaked distribution. That value indicates that only nearby pixels are significantly weighted during convolution. These values result in less blur and better preservation of details. Conversely, a large sigma value such as σ = 5 produces a wider, flatter distribution, indicating a greater influence of more distant pixels during convolution. This sigma value caused an increased blurring.Figure 6: POCA between two skew lines in 3D spaceFigure 7: Visualize the POCA point location in 2D projection(5)(2.5)

Proceedings of the Technical Sessions, 42 (2026) 81-89Institute of Physics, Sri Lanka 86End-to-End Simulation of Muon Scattering Tomography3.4 Simulated studiesIn muon scattering tomography, at least two tracking detector systems are required upstream and downstream of the object under inspection to reconstruct the muon trajectories before and after traversal. This configuration enables intrinsic 3D imaging and provides sensitivity to material composition through the scattering angle distribution and its dependence on the atomic number Z. The dataset consists of a GEANT4 model of an iron barrel containing cubic inclusions of uranium, lead and iron, with additional cubes of the same materials placed outside the barrel for reference and comparative analysis.4. RESULTS AND DISCUSSION4.1 Plotting the POCA points in a 2D histogramAs the shown in Figure 8, the multiple-projection of POCA points distribution was visualized in the 2-D histogram, with individual projection illustrated in the ??- plane, ??- plane and ??-plane. This visualization illustrates the POCA point distribution within the object. Each histogram is plotted as a color map, where the color intensity represents the number of POCA points in each voxel. Through this distribution, the areas of different densities within the object can be identified. Adding a color bar, helped to identify areas of higher and lower densities.4.2 Plot the POCA points as a 3-D point cloudIn this study, the POCA points were visualized as a three-dimensional point cloud as shown in Figure 9. The visualization of the POCA point 3D cloud allowed a detailed view of the spatial distribution of the object. To enhance visualization, transparency was applied to the POCA points. It was allowed to focus on the regions of interest where the POCA points are more concentrated. 4.3 Optimal values for Kernel sizeThe 3-D scattering density predictions on 2-D projections are plotted as shown the Figure 10.A single slice representing the brightest or most luminous image was selected for further analysis, and gaussian smoothing was applied to enhance the image and reduce noise.Figure 8: Spatial distribution of POCA points along the XY, XZ,YZ respectively

Proceedings of the Technical Sessions, 42 (2026) 81-89Institute of Physics, Sri Lanka 87End-to-End Simulation of Muon Scattering TomographyBased on Section 2.3 and observation of Figure 11, an appropriate σ range of 1 to 2 can be selected for the kernel size. The kernel size within this range balances smoothing and preserving meaningful details such as edges, peaks, or localized density features in the voxel.Figure 10: 3-D scattering density predictions on 2-D projectionsFigure 9: 3-D POCA points cloud in iron barrel

Proceedings of the Technical Sessions, 42 (2026) 81-89Institute of Physics, Sri Lanka 88End-to-End Simulation of Muon Scattering Tomography5. CONCLUSIONMuon Scattering Tomography (MST) is a non-destructive imaging technique that exploits the Coulomb scattering of cosmic-ray muons to reconstruct the internal density distribution and material composition of an object. In this study, the focus is given on the MST method, which exploits the Coulomb scattering of cosmic-ray muons to generate visualizations of the internal density distribution within target objects. The purpose of this project is to make an end-to-end simulation framework for scattering muography, using simulated data analysis techniques and track reconstruction algorithms. The spatial scattering characteristics were analyzed using POCA point distributions and visualized in both 2D and 3D, enabling effective material discrimination. Gaussian smoothing was applied to improve the accuracy of 3D scattering density predictions, with kernel sizes in the range 1 ≤ ? ≤ 2 providing an optimal balance between noise suppression and spatial resolution. The limitation of cosmic muon imaging technique is that a large number of detectors and long acquisition times are often required to obtain sufficient data for accurate imaging. Due to ongoing research and development efforts on MST technology, this project was carried out using simulation data.6. ACKNOWLEDGEMENTI am grateful to all who contributed to the successful completion of this research. I sincerely thank Dr. Andrea Giammanco of the Université Catholique de Louvain, Belgium, for his valuable guidance, support, and contributions throughout the project.Figure 11: The smoothed predictions of the selected slice after changing the kernel sizes

Proceedings of the Technical Sessions, 42 (2026) 81-89Institute of Physics, Sri Lanka 89End-to-End Simulation of Muon Scattering Tomography7. REFERENCES[1] L. Bonechi, R. D’Alessandro, and A. Giammanco, “Atmospheric muons as an imaging tool,” Nov. 01, 2020, Elsevier B.V. doi: 10.1016/j.revip.2020.100038.[2] P. Checchia et al., “INFN muon tomography demonstrator: Past and recent results with an eye to near-future activities,” 2019, Royal Society Publishing. doi: 10.1098/rsta.2018.0065.[3] S. Navas et al., “Review of particle physics,” Physical Review D, vol. 110, no. 3, Aug. 2024, doi: 10.1103/PhysRevD.110.030001.[4] A. Lechmann et al., “Muon tomography in geoscientific research – A guide to best practice,” Nov. 01, 2021, Elsevier B.V. doi: 10.1016/j.earscirev.2021.103842.[5] S. Barnes et al., “Cosmic-Ray Tomography for Border Security,” Mar. 01, 2023, MDPI. doi: 10.3390/instruments7010013.[6] M. Moussawi, A. Giammanco, V. Kumar, and M. Lagrange, “Muons for cultural heritage,” 2023. [Online]. Available: https://pos.sissa.it/452/[7] J. Bae and S. Chatzidakis, “Momentum-Dependent Cosmic Ray Muon Computed Tomography Using a Fieldable Muon Spectrometer,” Energies (Basel)., vol. 15, no. 7, Apr. 2022, doi: 10.3390/en15072666.[8] W. J. Jo, H. Il Kim, S. J. An, C. Y. Lee, C. H. Baek, and Y. H. Chung, “Design of a muon tomography system with a plastic scintillator and wavelength-shifting fiber arrays,” Nucl. Instrum. Methods Phys. Res. A, vol. 732, pp. 568–572, 2013, doi: 10.1016/j.nima.2013.05.115.[9] R. Muthugalalage, I. Darshana Gamage, “‘Development of single gap resistive plate chamber detectors for muography.’” [Online]. Available: http://hdl.handle.net/2078.1/284901[10] M. Lagrange, Muography_Workshop_BND_2023, GitHub repository, 2023. Available: https://github.com/MaximeLagrange/Muography_Workshop_BND_2023. Accessed: Feb. 27, 2026.[11] Y. Wang et al., “A High Spatial Resolution Muon Tomography Prototype System based on Micromegas Detector,” Dec. 2021, doi: 10.1109/TNS.2021.3137415.[12] C. Park, M. K. Baek, I. soo Kang, S. Lee, H. Chung, and Y. H. Chung, “Design and characterization of a Muon tomography system for spent nuclear fuel monitoring,” Nuclear Engineering and Technology, vol. 54, no. 2, pp. 601–607, Feb. 2022, doi: 10.1016/j.net.2021.08.029.

Proceedings of the Technical Sessions, 42 (2026) 90-95Institute of Physics, Sri Lanka 90Machine learning assisted identification of multi-layer 2D materials using optical microscopyMachine learning assisted identification of multi-layer 2D materials using optical microscopy Wathukarawatte K. H.1, Abeysinghe B.2, Gunawardana K. D. B. H.3, Sooriyagoda R. T.1, Munasinghe C. R.1* 1 Department of Physics, Faculty of Science, University of Colombo, Sri Lanka 2American Institutes for Research, United States 3Department of Physics and Electronics, Faculty of Science, University of Kelaniya, Sri Lanka [email protected]*1. ABSTRACTThis paper discusses the application of deep learning methods to the process of identifying graphene flakes and classifying the thickness of them using optical microscopy images. Two deep learning models, Mask R-CNN and YOLOv8, have been trained and tested on a dataset of 772 optical microscopy images that are classified into three different categories of graphene layer thickness (monolayer, few-layer and thick). Comprehensive preprocessing and augmentation procedures were used to enhance the generalization of the model under different imaging conditions. The Intersection over Union (IoU) measure and detection rates were used to assess model performance. YOLOv8 model recorded a higher overall mean average of the IoU at 0.757, than the Mask R-CNN model that recorded 0.614 as the average IoU. Although YOLOv8 was found to be faster in terms of detection and overall accuracy, especially in highdensity flake objects, Mask R-CNN offered more accurate pixel-level segmentation that can be used in morphological processing. The findings validate that machine learning could be used to characterize 2D materials in a scalable, cost-effective, and fast manner.Keywords: 2D materials, Deep Learning, Graphene Layer, Machine Learning, Optical Microscopy2. INTRODUCTIONTwo-dimensional (2D) materials, particularly graphene, have gained substantial interest due to their unique electronic, optical, and mechanical properties [1]. These properties are highly dependent on the number of stacked layers. For instance, monolayer graphene exhibits superior carrier mobility compared to multilayer graphene, which begins to exhibit bulk graphite behavior beyond ten layers [2]. Consequently, precise identification of layer count is essential for optimizing graphene’s performance in nanoelectronics and energy storage applications.Traditional identification methods such as Raman spectroscopy and Atomic Force Microscopy (AFM), while accurate, are time-consuming, expensive, and limited in throughput. Optical microscopy offers a non-destructive and accessible alternative; however, visual identification relies heavily on human expertise, leading to potential errors and inconsistencies [3].This research employs two state-of-the-art deep learning models, Mask R-CNN and YOLOv8, to automate the identification of multilayer graphene from optical microscopy images. Mask R-CNN is utilized for its instance segmentation capabilities, providing pixel-wise masks, while

Proceedings of the Technical Sessions, 42 (2026) 90-95Institute of Physics, Sri Lanka 91Machine learning assisted identification of multi-layer 2D materials using optical microscopyYOLOv8 is evaluated for its real-time object detection efficiency. By leveraging these models, this study aims to provide a rapid, scalable, and accurate method for 2D material characterization.3. METHODOLOGYa. DatasetThe study utilized a publicly available dataset [4] consisting of 772 training and 195 validation optical microscopy images. The dataset contains annotated segments classified into three categories: Monolayer, Few-layer (2-10 layers), and Thick Graphene. The images, originally 2040 x 1086 pixels, were preprocessed including normalization and resizing to 512 x 512 pixels to ensure compatibility with model architectures.b. Mask R-CNN ArchitectureMask R-CNN extends the Faster R-CNN architecture by adding a branch for predicting segmentation masks on each Region of Interest (RoI). The model was configured with a ResNet-101 backbone and a Feature Pyramid Network (FPN) for multi-scale feature extraction. Training was conducted using a Google Colab T4 GPU for 50 epochs. Key hyperparameters included a learning rate of 0.001, momentum of 0.9, and weight decay of 0.0001. The Region Proposal Network (RPN) anchor scales were set to range from 32 to 512 pixels to accommodate varying flake sizes.c. YOLOv8 ArchitectureYOLOv8 (You Only Look Once) is a single-shot detection algorithm that predicts bounding boxes and class probabilities directly from full images. Unlike Mask R-CNN, YOLOv8 utilizes an anchor-free detection mechanism. The model was trained on the same dataset, converted from COCO format to YOLO format (using bounding box coordinates). The training utilized the Cross Stage Partial Network (CSPNet) backbone for efficient feature extraction and a Path Aggregation Network (PAN) neck to enhance multi-scale representation.4. RESULTSa. Training ConvergenceThe training progression for both models indicated successful learning patterns. The Mask RCNN model showed a consistent decrease in both training and validation loss up to the 39th epoch, after which validation loss began to spike, indicating potential overfitting. Consequently, the weights from the 39th epoch were selected for evaluation. Similarly, the YOLOv8 model demonstrated steady improvements in bounding box regression and classification loss (box_loss, cls_loss), with the Mean Average Precision ([email protected]) showing a logarithmic growth trend, stabilizing towards the final epochs.

Proceedings of the Technical Sessions, 42 (2026) 90-95Institute of Physics, Sri Lanka 92Machine learning assisted identification of multi-layer 2D materials using optical microscopyb. Mask R-CNN PerformanceThe Mask R-CNN model achieved an overall average Intersection over Union (IoU) score of 0.614 across the test dataset. The detection rate for valid graphene segments was 59.72% (2221 out of 3719 segments detected). Analysis of the IoU distribution histograms for individual classes revealed a right-skewed distribution, particularly for the \"Thick Graphene\" class, indicating a high frequency of precise segmentations. However, the \"Few-Layer Graphene\" class exhibited a flatter distribution, suggesting the model struggled slightly more with distinguishing the subtle optical contrast differences in 2–10-layer flakes compared to monolayer or thick flakes.Figure 1: (a) Ground truth annototations (b) Predictions and whether the class was correct (Mask R-CNN)c. YOLOv8 PerformanceThe YOLOv8 model outperformed Mask R-CNN in detection metrics, achieving a global average IoU of 0.757. The overall detection rate was significantly higher at 69.72% (2593 out of 3719 segments detected). Class-specific performance was consistent, with average IoUs of 0.757 for Monolayer, 0.746 for Few-Layer, and 0.808 for Thick Graphene. Analysis of the F1-Confidence curve demonstrated an optimal balance between precision and recall at a confidence threshold of approximately 0.25, yielding a peak F1 score of 0.48. Furthermore, the model achieved a mean Average Precision ([email protected]) of 0.448, indicating robust detection capability despite the challenges of dataset annotation quality. Training metrics indicated high stability, with both bounding box regression loss and classification loss showing a smooth, monotonic decrease throughout the 50 epochs, unlike the validation spikes observed in the Mask R-CNN training. The confusion matrix further confirmed the model's reliability, revealing that the majority of errors were background misclassifications (false positives) rather than inter-class confusion between graphene layers.

Proceedings of the Technical Sessions, 42 (2026) 90-95Institute of Physics, Sri Lanka 93Machine learning assisted identification of multi-layer 2D materials using optical microscopyFigure 2: (a) Ground truth annototations (b) Predictions (YOLOv8)d. Comparative AnalysisTo evaluate robustness, the models were compared across images with varying flake densities.Table 1: IoU scores for images with different flake densitiesImage Index Flake Density Mask R-CNN IoU Yolov8 IoU246 Low 0.65 0.63425 Moderate 0.70 0.83176 High 0.38 0.69The comparison reveals a critical operational distinction: in low flake density images with few flakes (e.g., Image 246), both models performed comparably. However, in \"dense\" images with high flake counts (e.g., Image 176), YOLOv8 maintained a high IoU (0.69) while Mask R-CNN’s performance dropped significantly (0.38). This suggests YOLOv8 is superior for high-throughput screening of crowded substrates.5. DISCUSSIONa. Segmentation vs Detection Trade-OffThe results highlight a trade-off between semantic richness and detection robustness. While YOLOv8 demonstrated superior IoU scores and detection rates (roughly 10% higher than Mask R-CNN), it is limited to bounding box outputs. Mask R-CNN, despite lower statistical metrics, provides pixel-wise segmentation masks. This morphological data is essential for specific research applications, such as calculating the exact surface area of monolayer flakes for device fabrication, which a bounding box cannot provide.