NAS-Cap: Deep-Learning Driven 3-D Capacitance Extraction with Neural Architecture Search and Data Augmentation

TL;DR

This paper introduces NAS-Cap, a neural architecture search and data augmentation approach that significantly improves 3-D capacitance extraction accuracy and efficiency over previous CNN-based models, aiding integrated circuit design.

Contribution

It presents a novel NAS and data augmentation framework that enhances CNN models for 3-D capacitance extraction, outperforming prior methods in accuracy and computational efficiency.

Findings

NAS-Cap models achieve higher accuracy than CNN-Cap.

Models consume less runtime and storage space.

Architecture transferability maintains error reduction across designs.

Abstract

More accurate capacitance extraction is demanded for designing integrated circuits under advanced process technology. The pattern matching approach and the field solver for capacitance extraction have the drawbacks of inaccuracy and large computational cost, respectively. Recent work \cite{yang2023cnn} proposes a grid-based data representation and a convolutional neural network (CNN) based capacitance models (called CNN-Cap), which opens the third way for 3-D capacitance extraction to get accurate results with much less time cost than field solver. In this work, the techniques of neural architecture search (NAS) and data augmentation are proposed to train better CNN models for 3-D capacitance extraction. Experimental results on datasets from different designs show that the obtained NAS-Cap models achieve remarkably higher accuracy than CNN-Cap, while consuming less runtime for inference and space for model storage. Meanwhile, the transferability of the NAS is validated, as the once searched architecture brought similar error reduction on coupling/total capacitance for the test cases from different design and/or process technology.