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An Intelligent Optimization System Using Neural Networks and Soft Computing for the FMM Etching Process

Author

Listed:
  • Wen-Chin Chen

    (Ph.D. Program of Management, Chung Hua University, Hsinchu 30012, Taiwan)

  • An-Xuan Ngo

    (Ph.D. Program of Management, Chung Hua University, Hsinchu 30012, Taiwan)

  • Jun-Fu Zhong

    (Department of Industrial Management, Chung Hua University, Hsinchu 30012, Taiwan)

Abstract

The rapid rise of flexible AMOLED displays has prompted manufacturers to advance technologies to meet growing global demand. However, high costs and quality inconsistencies hinder industry competitiveness and sustainability. This study addresses these challenges by developing an intelligent optimization system for the fine metal mask (FMM) etching process, a critical step in producing high-resolution AMOLED panels. The system integrates advanced optimization techniques, including the Taguchi method, analysis of variance (ANOVA), back-propagation neural network (BPNN), and a hybrid particle swarm optimization–genetic algorithm (PSO-GA) approach to identify optimal process parameters. Experimental results demonstrate a marked improvement in product yield and process stability while reducing manufacturing costs. By ensuring consistent quality and efficiency, this system overcomes limitations of traditional process control; strengthens the AMOLED industry’s global competitiveness; and provides a scalable, sustainable solution for smart manufacturing in next-generation display technologies.

Suggested Citation

  • Wen-Chin Chen & An-Xuan Ngo & Jun-Fu Zhong, 2025. "An Intelligent Optimization System Using Neural Networks and Soft Computing for the FMM Etching Process," Mathematics, MDPI, vol. 13(13), pages 1-19, June.
    • Handle: RePEc:gam:jmathe:v:13:y:2025:i:13:p:2050-:d:1683852
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