A Python-Based Optimization Workflow for Tuning Analog Active Filter Circuits Using SPICE Simulation and Component Sensitivity Analysis

Abstract

This study presents a Python-based optimization workflow that integrates SPICE simulation and component sensitivity analysis to automate the tuning of analog active filter circuits. Traditional circuit design approaches rely heavily on manual trial-and-error adjustments, which are time-consuming and prone to error. To overcome these challenges, the proposed framework employs Python for automating SPICE simulations, performing sensitivity evaluations, and executing multi-objective optimization using the differential evolution algorithm. A second-order Sallen–Key active filter was used as a test case, with key performance parameters cutoff frequency, gain, phase margin, and quality factor serving as optimization objectives. The results showed that the optimized circuit achieved an 18.5% improvement in cutoff frequency accuracy, a 9.3% increase in gain stability, and a 17.9% enhancement in phase margin compared to the baseline design. Sensitivity analysis identified capacitive components (????₁ and ????₂) as the most influential parameters affecting circuit response, guiding targeted tuning for maximum efficiency. Monte Carlo simulations under ±5% component tolerances confirmed the optimized circuit’s robustness and performance stability. The convergence curve of the optimization process further validated the reliability of the differential evolution approach in achieving a global optimum. Overall, the proposed workflow bridges theoretical circuit design and practical implementation, offering a reproducible, open-source, and computationally efficient framework for analog circuit optimization. This research contributes to advancing automated analog design and intelligent electronic design automation (EDA) through the integration of data-driven simulation and optimization techniques.

Keywords: Python optimization, SPICE simulation, analog active filter, differential evolution, sensitivity analysis, circuit robustness, electronic design automation