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arXiv · 2505.03750

AI-Powered Agile Analog Circuit Design and Optimization

Jinhai Hu·Wang Ling Goh·Yuan Gao
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Abstract

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.

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Jinhai Hu, Wang Ling Goh, Yuan Gao. 2025-04-17. AI-Powered Agile Analog Circuit Design and Optimization. https://arxiv.org/abs/2505.03750

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