AI-Powered Agile Analog Circuit Design and Optimization

TL;DR

This paper demonstrates how AI techniques, specifically Bayesian optimization and transfer function modeling, can automate and improve analog circuit design and system-level optimization, reducing effort and enhancing performance.

Contribution

It introduces a novel integration of AI-assisted transistor sizing and transfer function modeling for joint analog and system-level optimization.

Findings

AI-assisted transistor sizing improves circuit performance.

Transfer function modeling enables system-level optimization within ML training.

AI reduces design iteration effort in analog circuit development.

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.