Artificial intelligence (AI) techniques are transforming analog circuit design by automating device-level tuning and enabling system-level co-optimization. This paper integrates two approaches: (1) AI-assisted transistor sizing using Multi-Objective Bayesian Optimization (MOBO) for direct circuit parameter optimization, demonstrated on a linearly tunable transconductor; and (2) AI-integrated circuit transfer function modeling for system-level optimization in a keyword spotting (KWS) application, demonstrated by optimizing an analog bandpass filter within a machine learning training loop. The combined insights highlight how AI can improve analog performance, reduce design iteration effort, and jointly optimize analog components and application-level metrics.
@article{arxiv.2505.03750,
title = {AI-Powered Agile Analog Circuit Design and Optimization},
author = {Jinhai Hu and Wang Ling Goh and Yuan Gao},
journal= {arXiv preprint arXiv:2505.03750},
year = {2025}
}