  The design automation of analog circuits is a longstanding challenge in the
integrated circuit field. This paper presents a deep reinforcement learning
method to expedite the design of analog circuits at the pre-layout stage, where
the goal is to find device parameters to fulfill desired circuit
specifications. Our approach is inspired by experienced human designers who
rely on domain knowledge of analog circuit design (e.g., circuit topology and
couplings between circuit specifications) to tackle the problem. Unlike all
prior methods, our method originally incorporates such key domain knowledge
into policy learning with a graph-based policy network, thereby best modeling
the relations between circuit parameters and design targets. Experimental
results on exemplary circuits show it achieves human-level design accuracy
(~99%) with 1.5x efficiency of existing best-performing methods. Our method
also shows better generalization ability to unseen specifications and
optimality in circuit performance optimization. Moreover, it applies to
designing diverse analog circuits across different semiconductor technologies,
breaking the limitations of prior ad-hoc methods in designing one particular
type of analog circuits with conventional semiconductor technology.
