Jinhai Hua, Wang Ling Goh
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{13760b4e-be89-4ecb-9f26-24c8f14ec5da,
title={AI-Powered Agile Analog Circuit Design and Optimization},
author={Jinhai Hua and Wang Ling Goh},
year={2025},
language={en}
}TY - JOUR TI - AI-Powered Agile Analog Circuit Design and Optimization AU - Jinhai Hua AU - Wang Ling Goh PY - 2025 LA - en ER -
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