Application of an Improved BP-AdaBoost Model in Semiconductor Quality Prediction

The semiconductor production data is typically complex, nonlinear and high-dimension, and the traditional post-sampling quality inspection often have large errors and will bring the economic losses caused by defective products. Based on machine learning technologies, this research is aimed to establish the proper model to predict semiconductor quality in advance. Based on the requirements of actual production, the BP neural network and AdaBoost algorithm are combined, and a new BP-AdqBoost model is proposed after optimizing the AdaBoost algorithm. The data of LCD Monitor production was analyzed. Then the improved BP-AdqBoost model, BP neural network, and the unmodified BP-AdaBoost prediction results are compared by prediction accuracy and reliability. The comparison shows that the improved BP-AdqBoost model can not only improve the prediction accuracy, but also strengthen the prediction reliability, which would be useful to the practical semiconductor production.