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In view of the increasingly prominent problems exposed by chips in integrated circuits and the destructive problems of traditional chip detection methods, a BP-AdaBoost neural network model based on genetic algorithm optimization was proposed and applied to chip detection and classification. By using electromagnetic probes to collect the electromagnetic signals generated by different chips in the same operating state and using the electromagnetic radiation signals as the basis for chip identification and classification, the signals are put into the BP-AdaBoost model optimized by genetic algorithm for learning and training. Experimental results show that this method has good effect in the application of chip recognition and classification.