Python-Based Surrogate Modeling of Nonlinear Analog Circuit Behavior for Fast Design Space Exploration

Nonlinear analog circuits are essential in modern electronic systems, but their design space is difficult to explore because circuit responses change strongly with device sizing, bias settings, and operating conditions. Repeated simulation-based exploration becomes slow and costly when many parameters must be tested across a broad design region. Recent studies have used machine learning, surrogate modeling, and data-driven optimization to reduce simulation cost in analog design. However, existing work still lacks a simple and practical Python-based framework that can model nonlinear analog circuit behavior accurately enough for fast and reliable design space exploration. To address this gap, this article presents a Python-based surrogate modeling framework for nonlinear analog circuit behavior and applies it to fast design space exploration. The study combines simulation data generation, preprocessing, surrogate training, validation, and prediction-guided exploration in one workflow. The results show strong agreement with simulated circuit responses, low prediction error across varying operating conditions, and clear exploration speedup compared with direct simulation-based search and baseline models. These findings show that Python-based surrogate modeling is an effective and practical approach for accelerating nonlinear analog design exploration and improving circuit design decision-making.