Towards knowledge-enhanced process models for semiconductor fabrication
2023 IEEE INTERNATIONAL INTERCONNECT TECHNOLOGY CONFERENCE, IITC AND IEEE MATERIALS FOR ADVANCED METALLIZATION CONFERENCE, MAM, IITC/MAM(2023)
摘要
We present a novel approach for modeling semiconductor processing that uses machine learning to combine expert knowledge, physics models, and actual process data into so-called knowledge-enhanced process models. Our method is illustrated on models for chemical-mechanical planarization, a key technology for semiconductor processing. It is an important step towards robust, accurate, and transferable, real-time models for digital twins of semiconductor processes and process chains.
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关键词
semiconductor process modeling, physics-informed machine learning, chemical-mechanical planarization
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