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 -
Siddhartha Mukherjee
This book provides a comprehensive overview of the critical aspects of industrial process engineering and plant design, addressing the complexities an
David Pollard, Geoff Dunlop
The MetPlant 2008 conference focused on advancements in metallurgical processing of ores, emphasizing plant design, operation strategies, and innovati
Unknown, Unknown
This chapter discusses metal casting processes, highlighting the diversity and common characteristics among them. The objective is to elucidate the fu
Rajesh Jha, Bimal Kumar Jha
This book describes the application of artificial intelligence (AI) and machine learning (ML) concepts to develop predictive models that can be used t
Unknown, Unknown
This study focuses on the fundamental characteristics of solid iron, which is predominantly composed of iron atoms and provides a basis for understand