Machine Learning Modeling and Run-to-Run Control of an Area-Selective Atomic Layer Deposition Spatial Reactor
Source abstract
Abstract
Semiconducting materials require stringent design specifications that make their fabrication more difficult and prone to flaws that are costly and damaging to their computing and electrical properties. Area-selective atomic layer deposition is a process that addresses concerns associated with design imperfections but requires substantial monitoring to ensure that process regulation is maintained. This work proposes a run-to-run controller with an exponentially weighted moving average method for an area-selective atomic layer deposition rotary reactor by adjusting the rotation speed of the substrate to control the growth per cycle of the wafer, which is calculated through a multiscale model with machine learning integration for pressure field generation and kinetic Monte Carlo simulations to increase computational efficiency. Results indicate that the run-to-run controller was able to bring the process to the setpoint when subjected to moderate pressure and kinetic shift disturbances.
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Research context
OpenAlex topics: Semiconductor materials and devices · Advancements in Semiconductor Devices and Circuit Design · Advanced Memory and Neural Computing
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Chemical and Biomolecular Engineering, University of California, Los Angeles, CA 90095, USA
Department of Chemical Engineering, Widener University, Chester, PA 19013, USA
Department of Electrical and Computer Engineering, University of California, Los Angeles, CA 90095, USA
Funding
National Science Foundation
References (47)
Show source-provided references
1. Silicon carbide semiconductors with wide bandgap for electric vehicles10.1007/s38314-021-0594-y ↗
2. Recent developments in TiO2 as n- and p-type transparent semiconductors: Synthesis, modification, properties, and energy-related applications10.1007/s10853-015-9303-7 ↗
3. Metal oxide semiconductor thin-film transistors for flexible electronics10.1063/1.4953034 ↗
4. Development of smart semiconductor manufacturing: Operations research and data science perspectives10.1109/ACCESS.2019.2933167 ↗
5. Nano-bioelectronics10.1021/acs.chemrev.5b00608 ↗
6. Two-dimensional semiconductors for transistors10.1038/natrevmats.2016.52 ↗
7. Khanna, V.K. (2016). Integrated Nanoelectronics: Nanoscale CMOS, Post-CMOS and Allied Nanotechnologies, Springer.10.1007/978-81-322-3625-2 ↗
8. Loubet, N., Hook, T., Montanini, P., Yeung, C.W., Kanakasabapathy, S., Guillom, M., Yamashita, T., Zhang, J., Miao, X., and Wang, J. (2017, January 5–8). Stacked nanosheet gate-all-around transistor to enable scaling beyond FinFET. Proceedings of the 2017 Symposium on VLSI Technology, Kyoto, Japan.10.23919/VLSIT.2017.7998183 ↗
9. Frieske, B., and Stieler, S. (2022). The “semiconductor crisis” as a result of the COVID-19 pandemic and impacts on the automotive industry and its supply chains. World Electr. Veh. J., 13.10.3390/wevj13100189 ↗
10. The global semiconductor chip shortage: Causes, implications, and potential remedies
11. Stuck in the middle: Taiwan’s semiconductor industry, the U.S.-China tech fight, and cross-strait stability10.1016/j.orbis.2020.11.005 ↗
12. Scarcity and global insecurity: The semiconductor shortage10.1109/MITP.2021.3105248 ↗
13. Atomic layer deposition: An overview10.1021/cr900056b ↗
14. A brief review of atomic layer deposition: From fundamentals to applications10.1016/j.mattod.2014.04.026 ↗
15. Atomic layer etching: An industry perspective10.1149/2.0021506jss ↗
16. Overview of atomic layer etching in the semiconductor industry10.1116/1.4913379 ↗
17. Achieving area-selective atomic layer deposition on patterned substrates by selective surface modification10.1063/1.1922076 ↗
18. Chemistry for positive pattern transfer using area-selective atomic layer deposition10.1002/adma.200502470 ↗
19. From the bottom-up: Toward area-selective atomic layer deposition with high selectivity10.1021/acs.chemmater.8b03454 ↗
20. Atomistic-mesoscopic modeling of area-selective thermal atomic layer deposition10.1016/j.cherd.2022.09.051 ↗
21. Multiscale modeling of spatial area-selective thermal atomic layer deposition10.1016/B978-0-443-15274-0.50012-3 ↗
22. Yun, S., Wang, H., Tom, M., Ou, F., Orkoulas, G., and Christofides, P.D. (2023). Multiscale CFD modeling of area-selective atomic layer deposition: Application to reactor design and operating condition calculation. Coatings, 13.10.3390/coatings13030558 ↗
23. Moyne, J., Del Castillo, E., and Hurwitz, A.M. (2018). Run-to-Run Control in Semiconductor Manufacturing, CRC Press.10.1201/9781420040661 ↗
24. Critical Manufacturing redefines semiconductor MES
25. Machine learning-based run-to-run control of a spatial thermal atomic layer etching reactor10.1016/j.compchemeng.2022.108044 ↗
26. Multivariable run-to-run control of thermal atomic layer etching of aluminum oxide thin films10.1016/j.cherd.2022.03.039 ↗
27. Integration of feedback control and run-to-run control for plasma enhanced atomic layer deposition of hafnium oxide thin films10.1016/j.compchemeng.2021.107267 ↗
28. Zhang, Y., Ding, Y., and Christofides, P.D. (2019). Integrating feedback control and run-to-run control in multi-wafer thermal atomic layer deposition of thin films. Processes, 8.10.3390/pr8010018 ↗
29. Area-selective atomic layer deposition of SiO2 using acetylacetone as a chemoselective inhibitor in an ABC-type cycle10.1021/acsnano.7b04701 ↗
30. Mechanism of precursor blocking by acetylacetone inhibitor molecules during area-selective atomic layer deposition of SiO210.1021/acs.chemmater.9b02992 ↗
31. Effect of deposition temperature and surface reactions in atomic layer deposition of silicon oxide using Bis(diethylamino)silane and ozone10.1016/j.apsusc.2021.151231 ↗
32. Temperature dependence of the sticking coefficients of bis-diethyl aminosilane and trimethylaluminum in atomic layer deposition10.1116/1.4971197 ↗
33. Ozone Adsorption on Graphene: Ab Initio Study and Experimental Validation10.1021/jp904321n ↗
34. A new algorithm for Monte Carlo simulation of Ising spin systems10.1016/0021-9991(75)90060-1 ↗
35. A general method for numerically simulating the stochastic time evolution of coupled chemical reactions10.1016/0021-9991(76)90041-3 ↗
36. Effects of Al precursors on deposition selectivity of atomic layer deposition of Al2O3 using ethanethiol inhibitor10.1021/acs.chemmater.0c02798 ↗
37. Surface diffusion control enables tailored-aspect-ratio nanostructures in area-selective atomic layer deposition10.1021/acsami.0c22121 ↗
38. Review on atomic layer deposition and applications of oxide thin films10.1080/10408436.2012.736886 ↗
39. Inhibitor-free area-selective atomic layer deposition of SiO2 through chemoselective adsorption of an aminodisilane precursor on oxide versus nitride substrates10.1016/j.apsusc.2022.152939 ↗
40. Integrated isothermal atomic layer deposition/atomic layer etching supercycles for area-selective deposition of TiO210.1021/acs.chemmater.9b01143 ↗
41. Exponentially weighted moving average (EWMA) control charts for monitoring an analytical process10.1021/ie070589b ↗
42. Run-to-run process control: Literature review and extensions10.1080/00224065.1997.11979749 ↗
43. A survey of run-to-run control for batch processes10.1016/j.isatra.2018.09.005 ↗
44. Montgomery, D.C. (2013). Introduction to Statistical Quality Control, John Wiley & Sons. [7th ed.].
45. Exponentially weighted moving average control schemes: Properties and enhancements
46. A multivariate self-tuning controller for run-to-run process control under shift and trend disturbances10.1080/15458830.1996.11770756 ↗
47. A study of R2R control improvement using adjustment limit to reduce frequency of control