Application of Support Vector Machines in High Power Device Technology - RAO Wei; LI Yong; YAN Ji (Kongzhi Yu Xinxi Jishu)
As a machine learning algorithm, support vector machine(SVM) has the advantages of good nonlinear processing ability, theoretical global optimum and overcoming the curse of dimensionality. It presented a support vector machines regression model (SVR) with Gauss kernel function (RBF). The best prediction model was obtained by normalization and dimensionality reduction for data and cross-validation for parameter optimization. The prediction model was tested and used to analyze the influence of process parameters on the qualified rate. Simulation results show that the SVR has high accuracy in the prediction of qualified rate and great significance to improve the quality of high power devices.