Machine Learning-Based Analog Layout Performance Prediction

Analog integrated circuit (IC) design faces critical efficiency bottlenecks in layout optimization, where the placement stage dictates circuit performance through parasitic effect, electromagnetic interactions, and device matching constraints. While modern automated placement tools prioritize geometric rules (e.g., symmetry, spacing), their iterative refinement cycles remain constrained by traditional evaluation workflows requiring full design rule checking (DRC), layout-versus-schematic (LVS) verification, parasitic extraction, and post-layout simulation. These latencies stem from nonlinear performance-layout interdependencies. To overcome this challenge, we develop a machine learning framework that directly predicts circuit performance metrics (gain margin, settling time) from geometric layout parameters, bypassing resource-intensive post-layout simulation workflows. Evaluated on open-source benchmarks, neural networks (NNs) demonstrate superior predictive accuracy with a mean squared error (MSE) of 320.16-51% and 35% lower than support vector machines (651.78) and random forests (490.45), respectively. By decoupling layout optimization from physical verification dependencies, this work establishes machine learning as a transformative accelerator for analog IC automation, with NNs emerging as preferred surrogates for performance-aware placement.