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Parameter optimization of etching process for a LGP stamper

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Abstract

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.

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References

  1. Zhuang D, Edgar JH (2005) Wet etching of GaN, AlN, and SiC: a review. Mater Sci Eng 48:1–46

    Article  Google Scholar 

  2. Sheu JT, You KS, Wu CH, Chang KM (2001) Optimization of KOH wet etching process in silicon nanofabrication. In: Proceedings of the 2001 1st IEEE Conference on Nanotechnology, M1.2 Nano-Mater I, pp 213–217

  3. Kim MJ, Kim DK, Lee SG, Kown OD (2004) Wet etching fabrication of photonic quantum ring laser. J Appl Phys 96:4742

    Article  Google Scholar 

  4. Wilke N, Mulcahy A, Ye SR, Morrissey A (2005) Process optimization and characterization of silicon microneedles fabricated by wet etch technology. Microelectron J 36:650–656

    Article  Google Scholar 

  5. Sakwe SA, Muller R, Wellmann PJ (2006) Optimization of KOH etching parameters for quantitative defect recognition in n-and p-type doped SiC. J Cryst Growth 289:520–526

    Article  Google Scholar 

  6. Chen WC, Chen HP, Chen XH (2010) Optimization of photolithography process for a LGP molding stamper. In: International conference on engineering and business management. Chengdu, China, pp 4626–4629

  7. Gandomi AH, Alavi AH (2012) A new multi-gene genetic programming approach to nonlinear system modeling. Part I: materials and structural engineering problems. Neural Comput Appl 21(1):171–187

    Article  Google Scholar 

  8. Smrekar J, Pandit D, Fast M, Assadi M, De S (2010) Prediction of power output of a coal-fired power plant by artificial neural network. Neural Comput Appl 19(5):725–740

    Article  Google Scholar 

  9. Han D, Moon SB, Park K, Kim B, Lee KK, Kim NJ (2005) Modelling of plasma etching process using radial basis function network and genetic algorithm. Vaccum 79:140–147

    Article  Google Scholar 

  10. Chen WC, Lee HI, Deng WJ, Liu KY (2006) The implementation of neural network for semiconductor PECVD process. Expert Syst Appl 32(4):1148–1153

    Article  Google Scholar 

  11. Chen WC, Chen CT, Chou SC (2010) A two-stage optimization system for the plastic injection molding. In: International conference on engineering and business management. Chengdu, China, pp 1600–1603

  12. Chen WC, Lai TT, Wang MW, Hung HW (2011) An optimization system for LED lens design. Expert Syst Appl 38(9):11976–11983

    Article  Google Scholar 

Download references

Acknowledgments

The authors would like to thank Material and Chemical Research Laboratories of Industrial Technology Research Institute in Taiwan for providing equipment and technical support.

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Correspondence to Min-Wen Wang.

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Chen, WC., Tai, YC., Wang, MW. et al. Parameter optimization of etching process for a LGP stamper. Neural Comput & Applic 23, 1539–1550 (2013). https://doi.org/10.1007/s00521-012-1103-2

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  • DOI: https://doi.org/10.1007/s00521-012-1103-2

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