Prediction of plasma enhanced deposition process using GA-Optimized GRNN

Authors:
Byungwhan Kim;Dukwoo Lee;Seung Soo Han
Affiliations:
Department of Electronic Engineering, Sejong University, Seoul, Korea;Department of Electronic Engineering, Sejong University, Seoul, Korea;Department of Information Engineering, Myongji University, Yongin, Korea
Venue:
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part III
Year:
2006

Citing 2
Cited 1

Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations

Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations
Genetic Algorithms in Search, Optimization and Machine Learning

Genetic Algorithms in Search, Optimization and Machine Learning

Adaptive Network-Based Fuzzy Inference Model of Plasma Enhanced Chemical Vapor Deposition Process

ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks

Quantified Score

Hi-index	0.00

Visualization

Abstract

A genetic algorithm (GA)-based optimization of generalized regression neural network (GRNN) was presented and evaluated with statistically characterized plasma deposition data. The film characteristics to model were deposition rate and positive charge density. Model performance was evaluated as a function of two training factors, the spread range and a factor employed for balancing training and prediction errors. For comparison, GRNN models were constructed as well as four types of statistical regression models. Compared to conventional GRNN models, GA-GRNN models improved the prediction accuracy considerably by about 50% for either film characteristic. The improvements over statistical regression models were more pronounced and they were more than 60%. There results clearly reveal that the presented technique can significantly improve conventional GRNN predictions.