אופטימיזציה נוירונית חוזרת למען תכנון סיגנל קודם-לייצור

תקציר מקורי באנגליתarXiv:2606.07463v2 Announce Type: replace-cross Abstract: Pre-layout design space exploration (DSE) for high-speed signal integrity (SI) analysis is often limited by the computational cost of simulations and iterative optimization algorithms within modern electronic design automation (EDA) workflows. While machine learning surrogate models accelerate the simulation step, optimizing designs still requires utilizing iterative black-box search methods. This iterative nature scales poorly and makes multi-corner sweeps computationally expensive. As a solution, this paper proposes amortized neural optimization (ANO) for pre-layout SI design. ANO avoids iterative black-box inference and utilizes a fully differentiable neural network surrogate model from which it extracts gradient information to t