Integrated Circuit Fault Diagnosis Model Based on Multilayer Perceptron

The traditional fault diagnosis methods in particular often fail to meet the needs of fast and accurate diagnosis for contemporary electronic devices. This paper attempts to study a fault diagnosis model based on multilayer perceptron. First, a large amount of circuit test data and fault samples are collected, and the data is cleaned and standardized to ensure data quality. Then, the input feature vector is constructed, including parameters such as voltage, current, and temperature. Next, multilayer perceptron is modelled, where activation functions such as ReLU and Softmax are implemented for nonlinear mapping. The model is trained using the back propagation algorithm, where weights and biases are optimized to reduce the mean square error. The performance of the model is evaluated with cross-validation, and its accuracy is further improved through hyperparameter setting regarding learning rate and batch size. The model reaches 95.2% accuracy in fault diagnosis, and the average diagnosis time is reduced by 30%. The multilayer perceptron-based fault diagnosis model has come out to be a superb ally in enhancing the performance of integrated circuit fault diagnosis and providing strong support for reliability in electronic equipment.