Academic Journal of Computing & Information Science, 2021, 4(6); doi: 10.25236/AJCIS.2021.040601.
Qianyi Cai, Ziquan Li
Changchun University of Science and Technology, Changchun, Jilin, 130022, China
Using a large amount of semiconductor signal data collected by sensors, effective data can be obtained by data cleaning and data mining. With the analysis of intelligent semiconductor detection model, the effective data can find out the problems in the production process of semiconductor manufacturing process in time, so as to improve the process yield and reduce the unit production cost. Firstly, this paper completes the data preprocessing, and then through the comparison of Pearson correlation coefficients, four variables with the largest Pearson correlation coefficients are obtained, which have the greatest influence on the results, so these four variables are taken as key factors. Finally, the nonlinear support vector machine model is established, and the hypersurface model in the input space corresponds to the hyperplane model in the feature space by using Gaussian function as the kernel function, so that the model has stronger robustness and better generalization ability.
Lagrange Interpolation Polynomial, Pearson Correlation Coefficient, Nonlinear Support Vector Machine
Qianyi Cai, Ziquan Li. Research on Semiconductor Quality Prediction Based on Nonlinear Support Vector Machine. Academic Journal of Computing & Information Science (2021), Vol. 4, Issue 6: 1-4. https://doi.org/10.25236/AJCIS.2021.040601.
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