论文信息 - Prediction of plasma processes using neural network and genetic algorithm

Prediction of plasma processes using neural network and genetic algorithm

Abstract Using genetic algorithm (GA) and backpropagation neural network (BPNN), computer models of plasma processes were constructed. The GA was applied to optimize five training factors simultaneously. The presented technique was evaluated with plasma etch data, characterized by a statistical experimental design. The etching was conducted in an inductively coupled plasma etch system. The etch outputs to model include aluminum (Al) etch rate, Al selectivity, silica profile angle, and DC bias. GA-BPNN models demonstrated improved predictions of more than 20% for all etch outputs but the DC bias. This indicates that a simultaneous optimization of training factors is more effective in improving the prediction performance of BPNN model than a sequential optimization of individual training factor. Compared to GA-BPNN models constructed in a previous training set, the presented models also yielded a much improved prediction of more than 35% for all etch outputs. The proven improvement indicates that the presented training set is more effective to improve GA-BPNN models.

Byungwhan Kim | Jungki Bae | Byungwhan Kim | J. Bae

[1] Thomas F. Edgar,et al. Modeling of Plasma Etch Systems Using Ordinary Least Squares, Recurrent Neural Network, and Projection to Latent Structure Models , 1997 .

[2] Gary S. May,et al. An optimal neural network process model for plasma etching , 1994 .

[3] Byungwhan Kim,et al. Prediction of etch microtrenching using a neural network , 2004 .

[4] Kwang-Ho Kwon,et al. Modeling oxide etching in a magnetically enhanced reactive ion plasma using neural networks , 2002 .

[5] D. E. Goldberg,et al. Genetic Algorithms in Search, Optimization & Machine Learning , 1989 .

[6] Byungwhan Kim,et al. Qualitative fuzzy logic model of plasma etching process , 2002 .

[7] Gary S. May,et al. Advantages of plasma etch modeling using neural networks over statistical techniques , 1993 .

[8] Byung-Teak Lee,et al. Modeling SiC surface roughness using neural network and atomic force microscopy , 2004 .

[9] Philipp Slusallek,et al. Introduction to real-time ray tracing , 2005, SIGGRAPH Courses.

[10] Byungwhan Kim,et al. An optimal neural network plasma model: a case study , 2001 .

[11] Byungwhan Kim,et al. Prediction of Plasma Etching Using a Classification-Based Neural Network , 2004 .

[12] David E. Goldberg,et al. Genetic Algorithms in Search Optimization and Machine Learning , 1988 .