# Parameters Adjustment Optimization for Prepreg of Copper Clad Laminate Based on Virtual Metrology
## 1 Introduction  
Trends in the printed circuit board (PCB) industry are obviously affected by the requirements of the manufacturers of computers, communication equipment, and consumer electronics. Due to global competition, the price pressure has increased in recent years. Therefore, reducing the manufacturing cost is very important. This research focuses on the manufacturing process of prepreg of copper-clad laminate (CCL) which is the core element of a PCB. Different prepregs are required for different specifications of base material or copper clad laminate. Companies have to produce various specifications of prepreg to satisfy customers' needs.  
Prepreg is produced by impregnating glass cloth with the epoxy resin. The prepreg can be further laminated with multi prepregs or copper foil to form the base material or CCL. The manufacturing process of the prepreg is illustrated by Figure 1.  
In Figure 1, the glass cloth is transferred from the left hand side by rollers. First, the glass cloth was impregnated with epoxy resin. Excess epoxy resin which attached to the glass cloth was then removed by two doctor rollers. The rolling direction of the two doctor rollers was opposite to the direction of motion of the glass cloth. Then the glass cloth, with the appropriate volume of epoxy resin, was transferred into a huge oven. In the oven, the epoxy resin was heated and dried. The prepreg was finally complete and was transferred out of the oven.  
There are a lot of important parameters for the prepreg manufacturing process. When different prepregs are produced successively in the same machine, setup time is required. Several parameters, including the temperatures of ovens, density of the epoxy resin, and gel time of the epoxy resin, are not easy to adjust and they are always changed while the machine is running. Therefore, the engineers have to adjust the other parameters based on the current temperature of the oven, density of the epoxy resin, and gel time of the epoxy resin, basing their judgments on their experience. Once the new parameters are set, a pilot run is required to ensure the quality of the prepregs. Two quality characteristics, ratio of resin and gel time, were taken into consideration in this research. If the prepregs did not meet the quality requirements, then the parameters needed to be adjusted again. Thus the setup time was increased and the efficiency reduced.  
It would be helpful if a prediction model could be built for predicting the performance of quality characteristics of the following lot of prepreg. The concept can be viewed as virtual metrology (VM). VM aims to predict the performance indicators by using parameters such as process variables and machine sensor data, and reduces the usage of physical measurement [1]. However, VM only provides the quality characteristics based on the given manufacturing parameters. Even with a VM model, the engineers need to know how to adjust the current parameters and make sure that the quality characteristics can be satisfied. Therefore, an optimization methodology for parameter adjustment is also required.  
In this research, a dataset of the prepreg manufacturing process was collected. Then the prediction models of VM were developed by support vector regression (SVR) and lasso regression (LR). Finally, mathematical models which integrate the corresponding VM models were proposed to optimize the parameter adjustment.