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[2602.12402] AstRL: Analog and Mixed-Signal Circuit Synthesis with Deep Reinforcement Learning
Summary
AstRL introduces a novel method for analog and mixed-signal circuit synthesis using deep reinforcement learning, significantly improving design metrics and structural correctness.
Why It Matters
This research addresses the growing complexity in circuit design, offering a solution that leverages deep reinforcement learning to enhance automation in analog and mixed-signal circuit synthesis. By optimizing design processes, it could lead to more efficient and reliable electronic systems, which are critical in modern computing and communications.
Key Takeaways
- AstRL uses deep reinforcement learning to optimize circuit design.
- The method generates structurally correct circuits with over 90% functionality.
- It addresses the challenges of diverse and non-differentiable circuit design spaces.
Computer Science > Machine Learning arXiv:2602.12402 (cs) [Submitted on 12 Feb 2026] Title:AstRL: Analog and Mixed-Signal Circuit Synthesis with Deep Reinforcement Learning Authors:Felicia B. Guo, Ken T. Ho, Andrei Vladimirescu, Borivoje Nikolic View a PDF of the paper titled AstRL: Analog and Mixed-Signal Circuit Synthesis with Deep Reinforcement Learning, by Felicia B. Guo and 3 other authors View PDF HTML (experimental) Abstract:Analog and mixed-signal (AMS) integrated circuits (ICs) lie at the core of modern computing and communications systems. However, despite the continued rise in design complexity, advances in AMS automation remain limited. This reflects the central challenge in developing a generalized optimization method applicable across diverse circuit design spaces, many of which are distinct, constrained, and non-differentiable. To address this, our work casts circuit design as a graph generation problem and introduces a novel method of AMS synthesis driven by deep reinforcement learning (AstRL). Based on a policy-gradient approach, AstRL generates circuits directly optimized for user-specified targets within a simulator-embedded environment that provides ground-truth feedback during training. Through behavioral-cloning and discriminator-based similarity rewards, our method demonstrates, for the first time, an expert-aligned paradigm for generalized circuit generation validated in simulation. Importantly, the proposed approach operates at the level of individ...