International Journal of Innovative Research in Computer and Communication Engineering
ISSN Approved Journal | Impact factor: 8.771 | ESTD: 2013 | Follows UGC CARE Journal Norms and Guidelines
| Monthly, Peer-Reviewed, Refereed, Scholarly, Multidisciplinary and Open Access Journal | High Impact Factor 8.771 (Calculated by Google Scholar and Semantic Scholar | AI-Powered Research Tool | Indexing in all Major Database & Metadata, Citation Generator | Digital Object Identifier (DOI) |
| TITLE | Digital Twin Implementation for Solar Cell Process Lines: Real-Time Simulation of PECVD, Diffusion, and Metallization for Predictive Process Control |
|---|---|
| ABSTRACT | Solar cell manufacturing is a continuous process flow with strong cell-to-cell coupling through shared equipment, shared consumables, and overlapping recipe boundaries. A perturbation introduced anywhere in the flow propagates downstream and surfaces in final electrical characterization with delay determined by production cadence. Digital twin technology reverses the temporal direction of the engineering loop - shifting from retrospective detection to predictive control - and delivers compounding second-order benefits across energy efficiency, recipe throughput, and anomaly sensitivity. This article presents the theoretical architecture, modeling methodology, and control framework for a production-grade digital twin spanning PECVD, diffusion, and screen-print metallization processes in a PERC cell manufacturing environment. The architecture is organized into four functionally decoupled layers: Physical, Data, Twin, and Application, communicating through a bidirectional closed-loop channel. The twin layer employs a hybrid modeling stack that fuses first-principles physics models with data-driven machine learning surrogates, calibrated continuously against per-wafer production measurements using Bayesian posterior update methods. The predictive control engine achieves end-to-end control loop latency below 540 milliseconds and a predictive horizon of 90 minutes ahead of measurement-driven detection. The Bayesian recipe optimization engine evaluates 340 candidate recipes per minute by querying the twin rather than the physical line, representing an orders-of-magnitude improvement in process optimization throughput. |
| AUTHOR | SEKHAR TATINENI Vice President, Technology, Greenwood, South Carolina, USA |
| VOLUME | 167 |
| DOI | DOI: 10.15680/IJIRCCE.2025.1303147 |
| pdf/147_Digital Twin Implementation for Solar Cell Process Lines Real-Time Simulation of PECVD, Diffusion, and Metallization for Predictive Process Control.pdf | |
| KEYWORDS | |
| References | [1] Grieves, M. (2014). Digital twin: Manufacturing excellence through virtual factory replication. White paper, Florida Institute of Technology. [2] Tao, F., Zhang, H., Liu, A., & Nee, A. Y. C. (2019). Digital twin in industry: State-of-the-art. IEEE Transactions on Industrial Informatics, 15(4), 2405–2415. [3] Rasheed, A., San, O., & Kvamsdal, T. (2020). Digital twin: Values, challenges and enablers from a modeling perspective. IEEE Access, 8, 21980–22012. [4] Madni, A. M., Madni, C. C., & Lucero, S. D. (2019). Leveraging digital twin technology in model-based systems engineering. Systems, 7(1), 7. [5] Boschert, S., & Rosen, R. (2016). Digital twin - The simulation aspect. In Mechatronic Futures (pp. 59–74). Springer. [6] Glaessgen, E. H., & Stargel, D. S. (2012). The digital twin paradigm for future NASA and U.S. Air Force vehicles. 53rd AIAA Structures, Structural Dynamics and Materials Conference. [7] Negri, E., Fumagalli, L., & Macchi, M. (2017). A review of the roles of digital twin in CPS-based production systems. Procedia Manufacturing, 11, 939–948. [8] Schleich, B., Anwer, N., Mathieu, L., & Wartzack, S. (2017). Shaping the digital twin for design and production engineering. CIRP Annals, 66(1), 141–144. [9] Chen, T., & Guestrin, C. (2016). XGBoost: A scalable tree boosting system. Proceedings of 22nd ACM SIGKDD, 785–794. [10] Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5–32. [11] Snoek, J., Larochelle, H., & Adams, R. P. (2012). Practical Bayesian optimization of machine learning algorithms. Advances in Neural Information Processing Systems, 25. [12] Kingma, D. P., & Ba, J. (2015). Adam: A method for stochastic optimization. ICLR 2015. [13] Preu, R., Lohmüller, E., Lohmüller, S., et al. (2020). Passivated emitter and rear cell - devices, technology, and modeling. Applied Physics Reviews, 7(4), 041315. [14] Dullweber, T., & Schmidt, J. (2016). Industrial silicon solar cells applying the passivated emitter and rear cell concept. IEEE Journal of Photovoltaics, 6(5), 1366–1381. [15] Crank, J. (1975). The Mathematics of Diffusion (2nd ed.). Oxford University Press. [16] Lieberman, M. A., & Lichtenberg, A. J. (2005). Principles of Plasma Discharges and Materials Processing (2nd ed.). Wiley-Interscience. [17] Macosko, C. W. (1994). Rheology: Principles, Measurements, and Applications. Wiley-VCH. [18] Page, E. S. (1954). Continuous inspection schemes. Biometrika, 41(1–2), 100–115. [19] Montgomery, D. C. (2020). Introduction to Statistical Quality Control (8th ed.). Wiley. [20] OPC Foundation (2017). OPC Unified Architecture Specification, Parts 1–14. OPC Foundation. [21] IEC 61512:1997. Batch control systems - Part 1: Models and terminology. International Electrotechnical Commission. [22] Inflation Reduction Act of 2022, Public Law 117-169. Section 45X Advanced Manufacturing Production Credit. [23] US Department of Energy (2024). Solar Energy Technologies Office: Annual Technology Baseline. National Renewable Energy Laboratory. [24] Green, M. A., Dunlop, E. D., Hohl-Ebinger, J., et al. (2024). Solar cell efficiency tables (Version 64). Progress in Photovoltaics, 32(7), 425–441. [25] Lee, J., Bagheri, B., & Kao, H. A. (2015). A cyber-physical systems architecture for Industry 4.0-based manufacturing. Manufacturing Letters, 3, 18–23. [26] Lasi, H., Fettke, P., Kemper, H. G., Feld, T., & Hoffmann, M. (2014). Industry 4.0. Business & Information Systems Engineering, 6(4), 239–242. |
