Parameter optimization of etching process for a LGP stamper - Chen, Wen-Chin; Tai, Yi-Chia; Wang, Min-Wen; Tsai, Hsiang-Cheng (Neural computing & applications, 2013)
This study proposes a two-stage system to optimize the etching process parameter for making a light guide plate (LGP) stamper. The multi-quality characteristics of the parameter settings include depth and uniformity of the microstructures formed in the LGP stamper. The control factors to conduct the process are etching temperature, specific gravity, spray pressure, transfer speed, and oscillating rate. The first stage is to conduct signal-to-noise (S/N) ratio optimization using Taguchi orthogonal array experiments. After conducting the etching process in microstructure, the experimental data can be translated and tested by back-propagation neural networks in order to create S/N ratio and the other quality characteristics predictors. In addition, the S/N ratio predictor and genetic algorithms are used together to obtain combinations of settings and to find the maximized process parameters on S/N ratios. As a result, the quality variance could be minimized. The second stage demonstrates quality characteristics optimization by pushing the process qualities to the targeted specifications. The analysis of variance (ANOVA) is employed to determine the significant control factors. Then, a statistical analysis using the aforementioned quality predictor, S/N ratios predictor, and particle swarm optimization is implemented to simulate the targeted specifications and then find a suitable specifications combination and the most stable and qualified process.