AI-Powered Agile Analog Circuit Design and Optimization
This work addresses analog circuit design challenges for engineers, offering incremental improvements through AI integration.
The paper tackles analog circuit design by integrating AI techniques for device-level tuning and system-level co-optimization, resulting in improved performance and reduced design effort, as demonstrated on a transconductor and a keyword spotting application.
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.