Parameter optimization of etching process for a LGP stamper

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...

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Vydané v:	Neural computing & applications Ročník 23; číslo 6; s. 1539 - 1550
Hlavní autori:	Chen, Wen-Chin, Tai, Yi-Chia, Wang, Min-Wen, Tsai, Hsiang-Cheng
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	London Springer London 01.11.2013 Springer
Predmet:	Applied sciences Artificial Intelligence Circuit properties Computational Biology/Bioinformatics Computational Science and Engineering Computer Science Computer science; control theory; systems Connectionism. Neural networks Data Mining and Knowledge Discovery Electric, optical and optoelectronic circuits Electronics Exact sciences and technology Image Processing and Computer Vision Integrated optics. Optical fibers and wave guides Microelectronic fabrication (materials and surfaces technology) Optical and optoelectronic circuits Original Article Probability and Statistics in Computer Science Semiconductor electronics. Microelectronics. Optoelectronics. Solid state devices Back-propagation neural networks Stamper Light guide plate Etching process Particle swarm optimization Taguchi orthogonal array Genetic algorithms Chemical etching Variance Relevance Backpropagation algorithm Plate waveguide Optical waveguide Swarm intelligence Uniformity Gravity Taguchi method Statistical analysis Neural network Experimental study Variance analysis Operating conditions Genetic algorithm Experimental design Signal to noise ratio
ISSN:	0941-0643, 1433-3058
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Popis
Shrnutí:	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.
ISSN:	0941-0643 1433-3058
DOI:	10.1007/s00521-012-1103-2