Semiconductor Material Performance Prediction Method Based on Genetic Algorithm

In recent years, the rapid development of nanotechnology and its widespread application in industrial production have made the functional prediction of nanomaterials a current research hotspot. This article trained and predicted Genetic Algorithm (GA). To improve the accuracy of experimental prediction, BP (Back Propagation) was introduced to establish a GA-BP neural network model. The GA-BP neural network was trained on the same training set to obtain an accurate GA-BP neural network model. The established GA-BP neural network was used to predict semiconductor material samples for comparison with actual values. In the comparison of conductivity type errors, material 1 had a conductivity type error of 3.48%, and material 2 had a conductivity type error of 3.76%. The results indicated that the predicted results of this method were very consistent with the actual values of semiconductor material properties.