Method for improving the accuracy of quality prediction in the manufacturing process of semiconductors

Abstract

This paper describes the high accurate Virtual Metrology (VM) technique we propose, which may be applied to a mass production process of multiple products, e.g. semiconductors.VM technology is necessary in a production process where sufficient amount of inspection data is not available. By applying VM technology, quality of the product and process error can be predicted based on the process data and relatively small amount of inspection data. Most common VM technology applied to these fields of manufacturing process for quality prediction uses a multivariate statistical model. However, the accuracy of prediction models using linear multiple regression drops when, during the generation of the model, a collinearity is found between terms in the process data that forms the explanatory variables. In addition, when updating the prediction model in order to increase its prediction accuracy, a collinearity among explanatory variables causes the regression coefficient to become unstable, resulting in a degradation of the prediction accuracy.The method proposed in this paper enables us to create a quality prediction model composed of mutually uncorrelated variables and to perform prediction while updating the coefficient of each variable in the prediction model formula.Our proposed technology will solve two problems: degradation of the quality prediction accuracy due to mutual interference between multiple process data and overfitting upon update of the metrology model.