An Open-Source AMS Circuit Optimization Framework Based on Reinforcement Learning—From Specifications to Layouts

Abstract

This paper presents a fully open-sourced AMS integrated circuit optimization framework based on reinforcement learning (RL). Specifically, given a certain circuit topology and target specifications, this framework optimizes the circuit in both schematic and post-layout phases. We propose using the heterogeneous graph neural network as the function approximator for RL. Optimization results suggest that it can achieve higher reward values with fewer iterations than the homogeneous graph neural networks. We demonstrate the applications of transfer learning (TL) in optimizing circuits in a different technology node. Furthermore, we show that by transferring the knowledge of schematic-level optimization, the trained RL agent can optimize the post-layout performance more efficiently than optimizing post-layout performance from scratch. To showcase the workflow of our approach, we extended our prior work to optimize latched comparators in the SKY130 and GF180MCU processes. Simulation results demonstrate that our framework can satisfy various target specifications and generate LVS/DRC clean circuit layouts.