[2603.24714] Can an Actor-Critic Optimization Framework Improve Analog Design Optimization?

Computer Science > Machine Learning arXiv:2603.24714 (cs) [Submitted on 25 Mar 2026] Title:Can an Actor-Critic Optimization Framework Improve Analog Design Optimization? Authors:Sounak Dutta, Fin Amin, Sushil Panda, Jonathan Rabe, Yuejiang Wen, Paul Franzon View a PDF of the paper titled Can an Actor-Critic Optimization Framework Improve Analog Design Optimization?, by Sounak Dutta and 5 other authors View PDF HTML (experimental) Abstract:Analog design often slows down because even small changes to device sizes or biases require expensive simulation cycles, and high-quality solutions typically occupy only a narrow part of a very large search space. While existing optimizers reduce some of this burden, they largely operate without the kind of judgment designers use when deciding where to search next. This paper presents an actor-critic optimization framework (ACOF) for analog sizing that brings that form of guidance into the loop. Rather than treating optimization as a purely black-box search problem, ACOF separates the roles of proposal and evaluation: an actor suggests promising regions of the design space, while a critic reviews those choices, enforces design legality, and redirects the search when progress is hampered. This structure preserves compatibility with standard simulator-based flows while making the search process more deliberate, stable, and interpretable. Across our test circuits, ACOF improves the top-10 figure of merit by an average of 38.9% over the stron...