Integrating support vector machine and genetic algorithm to implement dynamic wafer quality prediction system

The semiconductor and thin-film transistor liquid crystal display (TFT-LCD) industries are currently two of the most important high-tech industries in Taiwan and occupy over 40% of the global market share. Moreover, these two industries need huge investments in manufacture production equipments (PE) and own a large production scale of the global market. Therefore, how to increase the processing quality of PE to raise the production capacity has become an important issue. The statistical process control technique is usually adopted to monitor the important process parameters in the current Fabs. Furthermore, a routine check for machine or a predictive maintenance policy is generally applied to enhance the stability of process and improve yields. However, manufacturing system cannot be obtained online quality measurements during the manufacturing process. When the abnormal conditions occur, it will cause a large number of scrapped substrates and the costs will be seriously raised. In this research, a virtual metrology (VM) system is proposed to overcome those mentioned problems. It not only fulfills real-time quality measurement of each wafer, but also detects the performance degradation of the corresponding machines from the information of manufacturing processes. This paper makes four critical contributions: (1) The more principal component analysis (PCA) we used, the higher the accuracy we obtained by the kernel function approach; (2) the more support vector data description (SVDD) we used, the higher the accuracy we obtain in novelty detection module; (3) our empirical results show that genetic algorithm (GA) and incremental learning methods increase the training/learning of support vector machine (SVM) model; and (4) by developing a wafer quality prediction model, the SVM approach obtains better prediction accuracy than the radial basis function neural network (RBFN) and back-propagation neural network (BPNN) approaches. Therefore, this paper proposes that the artificial intelligent (AI) approach could be a more suitable methodology than traditional statistics for predicting the potential scrapped substrates risk of semiconductor manufacturing companies.