AI-Powered Agile Analog Circuit Design and Optimization

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{hu2025_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 }
}