Parameters Adjustment Optimization for Prepreg of Copper Clad Laminate Based on Virtual Metrology

Abstract

In this research, virtual metrology models, which can estimate the quality characteristics based on the given manufacturing parameters, were developed by lasso regression and support vector regression. Then, the virtual metrology (VM) models were integrated into a mathematical model to minimize the adjustment of manufacturing parameters and ensure the corresponding quality characteristics that would meet the customers’ needs. According to the results of the experiment, it was found that developing the virtual metrology model by using integration of lasso regression and support vector regression and taking second order terms into consideration produces the best performance for estimating the quality characteristics. Moreover, the integrated mathematical model can provide manufacturing parameters that minimize the amount of adjustment and ensure that the quality characteristics equal their corresponding target values. The proposed methodology can reduce the setup times and consequently increase productivity.

Keywords

• lasso regression
• prepreg
• printed circuit board
• support vector regression
• virtual metrology