A Digital Twin Framework With Bayesian Optimization and Deep Learning for Semiconductor Process Control
This paper introduces an intelligent optimization framework that integrates Digital Twin (DT) technology, deep learning, and a tailored Multi-Restart Bayesian Optimization with Random Initialization (MRBORI) to enhance parameter control and yield in semiconductor manufacturing. The proposed framewor...
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| Format: | Article |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10926511/ |
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| author | Chin-Yi Lin Tzu-Liang Tseng Tsung-Han Tsai |
| author_facet | Chin-Yi Lin Tzu-Liang Tseng Tsung-Han Tsai |
| author_sort | Chin-Yi Lin |
| collection | DOAJ |
| description | This paper introduces an intelligent optimization framework that integrates Digital Twin (DT) technology, deep learning, and a tailored Multi-Restart Bayesian Optimization with Random Initialization (MRBORI) to enhance parameter control and yield in semiconductor manufacturing. The proposed framework synergizes XGBoost-based feature selection, which identifies critical parameters in high-dimensional spaces, with a custom deep learning surrogate model that captures complex nonlinear interactions. Building on these insights, the MRBORI strategy leverages multiple optimization restarts, each initialized randomly, to mitigate local minima risks and systematically explore broad parameter spaces. Experimental validation using real-world data from an epitaxial silicon carbide (Epi SiC) process demonstrates notably tighter thickness control and improved yield compared to traditional methods. By unifying DT-driven real-time insights with advanced machine learning and multi-restart optimization, this framework offers a robust and precise solution for tackling the complexities of modern semiconductor manufacturing. |
| format | Article |
| id | doaj-art-eae2b8f1cd004f04a5ad3d236ceef714 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-eae2b8f1cd004f04a5ad3d236ceef7142025-08-20T03:42:18ZengIEEEIEEE Access2169-35362025-01-0113508215083710.1109/ACCESS.2025.355133210926511A Digital Twin Framework With Bayesian Optimization and Deep Learning for Semiconductor Process ControlChin-Yi Lin0https://orcid.org/0000-0002-5308-8531Tzu-Liang Tseng1https://orcid.org/0000-0002-3903-529XTsung-Han Tsai2https://orcid.org/0000-0001-9745-5957Department of Industrial Manufacturing and Systems Engineering, University of Texas at El Paso, El Paso, TX, USADepartment of Industrial Manufacturing and Systems Engineering, University of Texas at El Paso, El Paso, TX, USAInstitute of Information and Decision Sciences, National Taipei University of Business, Taipei, TaiwanThis paper introduces an intelligent optimization framework that integrates Digital Twin (DT) technology, deep learning, and a tailored Multi-Restart Bayesian Optimization with Random Initialization (MRBORI) to enhance parameter control and yield in semiconductor manufacturing. The proposed framework synergizes XGBoost-based feature selection, which identifies critical parameters in high-dimensional spaces, with a custom deep learning surrogate model that captures complex nonlinear interactions. Building on these insights, the MRBORI strategy leverages multiple optimization restarts, each initialized randomly, to mitigate local minima risks and systematically explore broad parameter spaces. Experimental validation using real-world data from an epitaxial silicon carbide (Epi SiC) process demonstrates notably tighter thickness control and improved yield compared to traditional methods. By unifying DT-driven real-time insights with advanced machine learning and multi-restart optimization, this framework offers a robust and precise solution for tackling the complexities of modern semiconductor manufacturing.https://ieeexplore.ieee.org/document/10926511/Digital twindeep learningmachine learningXGBoostmulti-restart Bayesian optimizationyield prediction |
| spellingShingle | Chin-Yi Lin Tzu-Liang Tseng Tsung-Han Tsai A Digital Twin Framework With Bayesian Optimization and Deep Learning for Semiconductor Process Control IEEE Access Digital twin deep learning machine learning XGBoost multi-restart Bayesian optimization yield prediction |
| title | A Digital Twin Framework With Bayesian Optimization and Deep Learning for Semiconductor Process Control |
| title_full | A Digital Twin Framework With Bayesian Optimization and Deep Learning for Semiconductor Process Control |
| title_fullStr | A Digital Twin Framework With Bayesian Optimization and Deep Learning for Semiconductor Process Control |
| title_full_unstemmed | A Digital Twin Framework With Bayesian Optimization and Deep Learning for Semiconductor Process Control |
| title_short | A Digital Twin Framework With Bayesian Optimization and Deep Learning for Semiconductor Process Control |
| title_sort | digital twin framework with bayesian optimization and deep learning for semiconductor process control |
| topic | Digital twin deep learning machine learning XGBoost multi-restart Bayesian optimization yield prediction |
| url | https://ieeexplore.ieee.org/document/10926511/ |
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