A Circuit Attention Network-Based Actor-Critic Learning Approach to Robust Analog Transistor Sizing

Abstract

Analog integrated circuit design is highly complex and its automation is a long-standing challenge. We present a reinforcement learning approach to automatic transistor sizing, a key step in determining analog circuit performance. A circuit attention network technique is developed to capture the impact of transistor sizing on circuit performance in an actor-critic learning framework. Our approach also includes a stochastic technique for addressing layout effect, another important factor affecting performance. Compared to Bayesian optimization (BO) and Graph Convolutional Network-based reinforcement learning (GCN-RL), two state-of-the-art methods, the proposed approach significantly improves robustness against layout uncertainty while achieving better post-layout performance. BO and GCN-RL can be enhanced with our stochastic technique to reach solution quality similar to ours, but still suffer from a much slower convergence rate. Moreover, the knowledge transfer in our approach is more effective than that in GCN-RL.