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Building neural network equipment models using model modifier techniques

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2 Author(s)
M. Marwah ; Dept. of Mech. Eng., Colorado Univ., Boulder, CO, USA ; R. L. Mahajan

In this paper, we address the problem of developing accurate neural network equipment models economically. To this end, we propose model modifier techniques in conjunction with physical-neural network models. Two model modifiers-difference method and source input method-are proposed and evaluated on a horizontal chemical vapor deposition reactor. The results show that the source input method outperforms the difference method. Further, to develop a model of comparable accuracy, the source input method reduces the number of experimental data points to approximately one fourth of those needed without this approach

Published in:

IEEE Transactions on Semiconductor Manufacturing  (Volume:12 ,  Issue: 3 )