Analog circuit faults are the main cause of performance degradation or paralysis in integrated circuit systems. However, due to the complex causes and diverse manifestations of circuit faults themselves, traditional methods have high difficulty in identifying typical faults in analog circuits and low recognition accuracy. This article constructs an improved ResNet deep feature recognition network model and establishes one-dimensional and two-dimensional fault information sources. Finally, particle swarm optimization algorithm is used to search for the optimal parameters solved by the model, ultimately achieving improvements in the accuracy and recognition speed of analog circuit fault diagnosis. Finally, through experimental verification, the recognition accuracy of typical fault C2 reached 99.6%, proving the effectiveness of the method proposed in this paper.
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