Color Filter Polishing Optimization Using ANFIS With Sliding-Level Particle Swarm Optimizer

• Published in: IEEE transactions on systems, man, and cybernetics. Systems Vol. 50; no. 3; pp. 1193 - 1207
• Main Authors: Tsai, Jinn-Tsong, Chou, Ping-Yi, Chou, Jyh-Horng
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.03.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adaptive filters; Adaptive network-based fuzzy inference system (ANFIS); Adaptive systems; Algorithms; chemical mechanical polishing (CMP); Chemical-mechanical polishing; Chemicals; Color; color filter (CF); Fuzzy systems; Glass; Image color analysis; Industrial applications; Neural networks; Optimization; Organic chemistry; Particle swarm optimization; particle swarm optimization (PSO); Pixels; Process parameters; Sliding; sliding-level (SL) method; Slurries; Substrates; Surface roughness
• ISSN: 2168-2216; 2168-2232
• DOI: 10.1109/TSMC.2017.2776158

An adaptive network-based fuzzy inference system (ANFIS) with a sliding-level particle swarm optimization (SL-PSO) is proposed for optimizing parameters of a chemicalmechanical process for polishing a color filter (CMP-CF). The SL-PSO is used not only to find the best membership function types, but also to optimize the premise and consequent parameters for ANFIS. The important process parameters for CMP-CF included the initial time, polishing time, polishing pad weight (down force), slurry chemicals, and rotation speed. The output targets were red pixels, green pixels, blue pixels, and the surface roughness. First, the performance of the SL-PSO was tested with 18 continuous global numerical optimization problems, including six unimodal functions, seven multimodal functions, and five complex rotated and shifted functions. Nonparametric Wilcoxon tests were also used in multiple-problem analysis for simultaneous comparison of various algorithms over a problem set. The computational experiments showed that the proposed SL-PSO approach outperforms PSO-based methods reported in the literature. Finally, the proposed SL-PSO method was used to optimize CMP-CF parameters. The experimental results showed that the ANFIS with SL-PSO outperforms the conventional ANFIS method and conventional back propagation neural network in terms of prediction accuracy. A practical industrial application in a CF manufacturer showed that the ANFIS with SL-PSO obtained superior results compared to the previous method and immediately enhanced production efficiency. Together, these experimental results indicate that the proposed ANFIS with SL-PSO is a reliable method for optimizing CMP-CF processes.