標題: Intelligent Manufacturing: TCAD-Assisted Adaptive Weighting Neural Networks
作者: Huang, Chien Y.
Fu, Sze M.
Parashar, Parag
Chen, Chun H.
Akbar, Chandni
Lin, Albert S.
電子工程學系及電子研究所
Department of Electronics Engineering and Institute of Electronics
關鍵字: Machine learning algorithms;artificial neural networks;semiconductor device manufacture;semiconductor process modeling
公開日期: 1-Jan-2018
摘要: Using machine intelligence on device and process performance prediction is an emerging methodology in the IC industry. While semiconductor technology computer-aided design (TCAD) has been researched and developed for over 30 years, it should contribute to or be used in conjunction with machine learning algorithms in solution finding procedure. Here, we propose an adaptive weighting neural network (AWNN) model that combines the advantages of statistical the machine learning model and the physical TCAD model. Using aspect ratio dependent etching as an example, our proposed AWNN outperforms conventional artificial neural network in terms of mean square errors in the test set where 5-10 times reduction is observed. The effectiveness of the TCAD AWNN model can be especially effective in the case of sampling over a vast sample space since the under-sampling problem can be compensated by the TCAD model. The large and nearly unbounded sample space is very common in IC technology, where cascaded and repeated process steps exist (similar to 150 process steps and similar to 20 masks for 90-nm CMOS process).
URI: http://dx.doi.org/10.1109/ACCESS.2018.2885024
http://hdl.handle.net/11536/148665
ISSN: 2169-3536
DOI: 10.1109/ACCESS.2018.2885024
期刊: IEEE ACCESS
Volume: 6
起始頁: 78402
結束頁: 78413
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