Training Better CNN Models for 3-D Capacitance Extraction with Neural Architecture Search

Haoyuan Li, Dingcheng Yang, Wenjian Yu

Abstract: More accurate capacitance extraction is demanded for IC design nowadays. The pattern matching approach and the field solver for capacitance extraction have the drawbacks of in-accuracy and large computational cost, respectively. Recent work [1] proposes a grid-based data representation and a convolutional neural network based capacitance models (called CNN -Cap) for 3- D capacitance extraction. In this work, the techniques of neural architecture search (NAS) is proposed to train better models for 3- D capacitance extraction. Experimental results show that the obtained NAS-Cap model achieves higher accuracy than [1].