AnaCraft: Duel-Play Probabilistic-Model-Based Reinforcement Learning for Sample-Efficient PVT-Robust Analog Circuit Sizing Optimization

Recent advancements in machine learning offer the potential for finding faster and robust optimization approaches for analog circuit design automation. However, fully automated yet fast and process, voltage, and temperature (PVT)-robust sizing algorithms are still lacking as even the most recent methods continue to require extensive simulations or domain-specific circuit expertise. In this article, we present a PVT-robust analog circuit sizing method, called AnaCraft, that is the first to introduce an adversarial training scheme of multiagent reinforcement learning (RL) for robust circuit design automation. We adopt the soft actor–critic (SAC) agent for circuit sizing, which outperforms other actor–critic agents in stability and robustness. Then, we introduce a duel-play scheme to address PVT-robustness, where sizing agents cooperate to find optimal circuit parameters while competing with an adversarial PVT agent. We combine this approach with the model-based policy optimization method: an ensemble of probabilistic models is trained and used to extract many short rollouts of generated data for updating the sizing agents. We test our algorithm on the sizing of operational amplifiers in a 45-nm CMOS technology, as well as on a complex data receiver circuit in a predictive 7-nm FinFET technology. This demonstrates our approach’s ability to find PVT-robust power-area-optimal sizes for advanced technologies and circuits. Our proposed method achieves a higher figure of merit with up to $\sim 3\times$ fewer circuit simulations and $\sim 2\times$ less runtime compared to existing state-of-the-art methods.