Deep Reinforcement Learning for Analog Circuit Structure Synthesis

Abstract

This paper presents a novel deep-reinforcement-learning-based method for analog circuit structure synthesis. It behaves like a designer, who learns from trials, derives design knowledge and experience, and evolves gradually to eventually figure out a way to construct circuit structures that can meet the given design specifications. Necessary design rules are defined and applied to set up the specialized environment of reinforcement learning in order to reasonably construct circuit structures. The produced circuit structures are then verified by the simulation-in-loop sizing. In addition, hash table and symbolic analysis techniques are employed to significantly promote the evaluation efficiency. Our experimental results demonstrate the sound efficiency, strong reliability, and wide applicability of the proposed method.