Wafer Reflectance Prediction for Complex Etching Process Based on K-Means Clustering and Neural Network

In LED semiconductor manufacturing, it is important to evaluate the wafer reflectance in order to validate the quality of wafers. In this work, a learning model based on K-means clustering and neural networks is proposed to analyze the relationship between etching parameters and wafer reflectance under a complex etching environment. The implemented clustering algorithm is used to cluster historical data and reduce the effect caused by different processing environments. Then, for each obtained cluster, a neural network is developed to learn the relationship between etching parameters and wafer reflectance. Finally, a real case study from an LED semiconductor fab is conducted to show the application of the proposed method. Experiments show that the proposed method achieves much better performance in comparison with support vector machine for mapping the relationship between etching parameters and wafer reflectance. Also, by the proposed method, the average prediction accuracy of the wafer reflectance can be improved by up to 9.38%, and the mean square error is reduced by 21.64% compared with the methods without clustering.