Towards knowledge-enhanced process models for semiconductor fabrication

Abstract

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.