Analog Circuits Fault Diagnosis Based on Machine Learning

This paper delves into an innovative method for diagnosing faults in analog circuits, relying on the measurement of S-parameters and the comprehensive analysis using traditional machine learning and neural networks with residual structures. Through this approach, we were able to accurately identify which resistor in the circuit experienced a hard fault, either short or open. We tested and evaluated the performance of multiple classifiers in a common-emitter amplifier circuit, and the results demonstrated that both classification methods can achieve up to $97.78 \%$ accuracy in identifying analog circuit faults.