RLCkt: An Analog Circuit Automatic Sizing Sage Based on Reinforcement Learning

Abstract

Automated solutions for sizing analog circuits have gained significant interest due to the labor-intensive nature of the task in a typical design cycle, especially with technology development and circuit scaling. This study introduces the RLCkt, an Analog Circuit Automatic Sizing Sage, which utilizes reinforcement learning (RL) and the SPICE simulator to automatically adjusts design parameters to meet performance metrics. After several hours of training, RLCkt runs 94 times faster than a traditional generic algorithm with comparable performance. Additionally, its generalization capability surpasses that of state-of-the-art methods.