MODELING OF THIN FILM PROCESS DATA USING A GENETIC ALGORITHM-OPTIMIZED INITIAL WEIGHT OF BACKPROPAGATION NEURAL NETWORK

REFERENCES

• Bartlett , P. L . 1998 . The sample complexity of pattern classification with neural networks: The size of the weights is more important than the size of the network . IEEE Trans Information Theory 44 ( 2 ): 525 – 536 .
   Web of Science ®Google Scholar
• Geisler , P. C. S. G. Lee , and G. S. May . 2000 . Neurofuzzy modeling of chemical vapor deposition process . IEEE Trans. Semicond. Manufact. 13 ( 1 ): 46 – 60 .
   Google Scholar
• Goldberg , D. E . 1989 . Genetic Algorithms in Search, Optimization & Machine Learning . Boston , MA , USA : Addison-Wesley .
   Google Scholar
• Han , S. S . and G. S. May . 1997 . Using neural network process models to perform PECVD silicon dioxide recipe synthesis via genetic algorithms . IEEE Trans. Semicond. Manufact. 10 ( 2 ): 279 – 287 .
   Web of Science ®Google Scholar
• Himmel , C. D. and G. S. May . 1993 . Advantages of plasma etch modeling using neural networks over statistical techniques . IEEE Trans. Semicond. Manufact. 6 ( 2 ): 103 – 111 .
   Web of Science ®Google Scholar
• Kim , D . 2005 . Improving prediction performance of neural networks in pattern classification . Internat. J. Com. Math 82 ( 4 ): 391 – 399 .
   Web of Science ®Google Scholar
• Kim , B. and J. Bae . 2005 . Prediction of plasma processes using neural network and genetic algorithm . Solid-State Electronics. 49 ( 10 ): 1576 – 1580 .
   Google Scholar
• Kim , B. and S. Park . 2001 . An optimal neural network plasma model: A case study . Chemometr. Intell. Lab. Syst. 56 ( 1 ): 39 – 50 .
   Google Scholar
• Kim , B. , J. Bae , and S. S. Han . 2006. Prediction of radio frequency power effect on silicon nitride deposition using a genetic algorithm based neural network. Surf. Eng. February:63–68.
   Google Scholar
• Kim , B. , J. Park , and K. Lee . 2006 . Temperature effect on deposition rate of silicon nitride film . Appl. Surf. Sci. 252 ( 12 ): 4138 – 4145 .
   Google Scholar
• Montgomery , D. C. 1991 . Design and Analysis of Experiments . New York : John Wiley & Sons .
   Google Scholar
• Rietman , E. and E. Lory . 1993 . Use of neural networks in semiconductor manufacturing processes: An example for plasma etch modeling . IEEE Trans. Semicond. Manufact. 16 ( 4 ): 343 – 347 .
   Google Scholar
• Rummelhart , D. E. and J. L. McClelland . 1986 . Parallel Distributed Processing . Cambridge : MIT. Press .
   Google Scholar
• Sarkar , D. 1996 . Randomness in generalization ability: A source to improve it . IEEE Trans. Neural Networks 7 ( 3 ): 676 – 685 .
   PubMedGoogle Scholar
• Teo , K. K , L. P Wang , and Z. P. Lin . 2001 . Wavelet packet multi-layer perceptron for chaotic time series prediction: Effects of weight initialization . Lecture Notes in Computer Science. 2074 : 310 – 317 .
   Google Scholar
• Venkateswaran , S. , M. M. Rai , T. R Govindan , and M. Meyyappan . 2002 . Neural network modeling of growth process . J. Electrochem. Soc. 149 ( 2 ): G137 – G142 .
   Google Scholar