Intelligent Manufacturing: TCAD-Assisted Adaptive Weighting Neural Networks

• Published in: IEEE access Vol. 6; pp. 78402 - 78413
• Main Authors: Huang, Chien Y., Fu, Sze M., Parashar, Parag, Chen, Chun H., Akbar, Chandni, Lin, Albert S.
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2018; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adaptation models; Algorithms; Artificial neural networks; Aspect ratio; CAD; CMOS; Computer aided design; Etching; Integrated circuits; Intelligent manufacturing systems; Machine learning; Machine learning algorithms; Neural networks; Performance prediction; Sampling; semiconductor device manufacture; Semiconductor device modeling; Semiconductor process modeling; Training; Weighting
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2018.2885024

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 (~150 process steps and ~20 masks for 90-nm CMOS process).