Semiconductor fabrication process multi-performance prediction method

TL;DR: In this article, the authors proposed a multi-performance prediction model for semiconductor fabrication process using a Bayes neural network, and analyzed whether the model performance conforms to performance evaluation criteria by a model precision proof method, and performed online correction on the network model structures which do not conform to the standard.

Abstract: The invention relates to a semiconductor fabrication process multi-performance prediction method, comprising the steps of selecting an articles being processed level parameter, an equipment parameter and a workpiece parameter which represent the state of a semiconductor production line as influence factors of performance indexes; collecting relevant data of the production line, preprocessing by using a principal component analysis method, removing redundant information, constructing a multi-performance prediction model by using a Bayes neural network, and controlling the complexity of the network model by introducing a Bayes method; analyzing whether the model performance conforms to a performance evaluation criteria by a model precision proof method, and performing online correction on the network model structures which do not conform to the standard; finally determining the key factors influencing the average workpiece processing period and the equipment utilization rate. According to the semiconductor fabrication process multi-performance prediction method, the defects that the performance prediction model in the semiconductor field is limited by constraint conditions, the generalization performance is very poor and the like are improved, the problem that the single performance prediction model in the semiconductor field is not applicable to multi-performance prediction modeling is solved, and the method is an improvement of the semiconductor fabrication process multi-performance prediction method.