RoSE: Robust Analog Circuit Parameter Optimization with Sampling-Efficient Reinforcement Learning

Design automation of analog circuits has been a long-standing challenge in the integrated circuit field. Recently, multiple methods based on learning or optimization have demonstrated great promise in automating device sizing for analog circuits. However, they often ignore the strong susceptibility of analog circuits to process, voltage, and temperature (PVT) variations or suffer from low sampling efficiency to train algorithms. To address these critical limitations, this paper proposes RoSE, the first Robust analog circuit parameter optimization framework with high Sampling Efficience by synergistically combining Bayesian Optimization (BO) and reinforcement learning (RL). Its core is to use the fast convergence of BO to find an optimized starting point for the backbone RL agent to notably improve its sampling efficiency during the learning process. With this pre-optimization, we further leverage the RL's superior optimization ability to achieve robust device sizing by incorporating sufficient features of PVT variations into the representation learning loop. Experimental results of our proposed method on exemplary circuits show 3.25x similar to 16x improvement of sampling efficiency and 6.8x similar to 24x improvement of figure-of-merit (FoM, defined with design efficiency and design accuracy) as compared to prior methods.