A Novel Test Stimulus Generation Method Based On Ensemble Learners for Analog Circuits

Test stimulus generation is a very effective tool in analog circuit fault diagnosis. The extensive usage of machine learning methods in analog circuit fault diagnosis has verified their good interpretability and effectiveness. This paper proposes a test stimulus generation method for analog circuits based on integrating four machine- learning methods. It selects frequency by classifying the frequency domain signals, fusion of feature importance, and one-to-one correspondence between features and frequencies. To verify the effectiveness of the proposed method, it was validated using the Sallen-Key bandpass filter circuit and the four-op-amp biquadratic high-pass circuit. The experimental results show that the test stimulus obtained by the proposed method can effectively excite the fault features and improve the diagnosis accuracy.