Automatic Modeling of Logic Device Performance Based on Machine Learning and Explainable AI
| Autor: | Youngkyu Shin, Dong-Won Kim, Sangwon Baek, Jae-hoon Jeong, Myung-Gil Kang, Kwangseok Lee, Keun Hwi Cho, K. J. Chang, Hyeon-Kyun Noh, Daesin Kim, Seung-ju Kim |
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| Rok vydání: | 2020 |
| Předmět: |
010302 applied physics
Calibration (statistics) Computer science business.industry Process (computing) 02 engineering and technology Work in process 021001 nanoscience & nanotechnology Machine learning computer.software_genre 01 natural sciences Shapley value Process conditions 0103 physical sciences Artificial intelligence 0210 nano-technology business computer |
| Zdroj: | 2020 International Conference on Simulation of Semiconductor Processes and Devices (SISPAD). |
| DOI: | 10.23919/sispad49475.2020.9241681 |
| Popis: | In this paper, we propose a machine learning framework for predicting performances of semiconductor devices that can automatically reflect modifications in process conditions. While standard TCAD simulators require intensive modeling and calibration works to capture new process conditions, our proposed framework can learn these conditions from data efficiently and directly. Furthermore, by applying recently attention-getting explainable AI techniques, important factors that affecting device performances can be discovered automatically from the proposed model. Specifically, our model quantifies significance of each process step as the game-theoretic Shapley value, that cannot be achieved by TCAD simulators. |
| Databáze: | OpenAIRE |
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