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

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].