Accurate and scalable fault diagnosis in analog integrated circuits (AICs) remains a significant challenge, particularly in detecting soft parametric faults arising from process variations, aging, and environmental factors. This paper presents a comprehensive machine learning–based framework for automated fault classification in linear analog circuits, demonstrated through two representative case studies, an RC band-pass filter and a Butterworth low-pass filter. Frequency-domain responses of both output voltage and supply current were analysed, and complex-valued features comprising real and imaginary components were extracted to capture the circuits' resistive and reactive characteristics.
Monte Carlo simulations with ±30 % component deviations generated a rich dataset for training and validation. Nine machine learning classifiers, including CatBoost, LightGBM, and XGBoost, were benchmarked against traditional approaches. The proposed complex-domain feature extraction method significantly outperformed magnitude-only and real-part-only baselines, with CatBoost achieving the highest accuracy of 99.75 %. Computational efficiency and inference analysis confirmed the model's suitability for real-time fault diagnosis, with millisecond-level latency and compact model size. SHAP (SHapley Additive exPlanations) analysis provided interpretability by identifying the most influential spectral features contributing to fault classification.
Finally, the framework's generalisation and practical feasibility were demonstrated through cross-circuit evaluation and a hardware validation perspective, outlining measurement procedures and highlighting its real-world applicability. The results confirm that the proposed approach effectively integrates high diagnostic accuracy, interpretability, and computational efficiency, establishing a robust and explainable solution for fault diagnosis in linear analog circuits.
扫码关注我们
求助内容:
应助结果提醒方式:
