Prediction method of near field photolithography line...

Details Prediction method of near field photolithography line... Prediction method of near field photolithography line...

2007-06-11

2010-06-29

Patel, Ramesh B (Department: 2121)

Data processing: structural design, modeling, simulation, and em

Simulating nonelectrical device or system

C703S002000, C706S013000, C706S015000, C706S021000, C700S044000, C700S048000

Reexamination Certificate

active

07747419

ABSTRACT:
A method of building a set of experimental prediction model that requires fewer experimental frequency, shorter prediction time and higher prediction accuracy by using the advantages of combining the experimental data of Taguchi method and neural network learning is disclosed. The error between the experimentally measured result of photolithography and the simulated result of the theoretical model of near field photolithography is set as an objective function of an inverse method for back calculating fiber probe aperture size, which is adopted in the following Taguchi experiment. The analytical result of Taguchi neural network model of the present invention proves that the Taguchi neural network model can provide more accurate prediction result than the conventional Taguchi network model, and at the same time, improve the demerit of requiring massive training examples of the conventional neural network.

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“Applying ANN/GA Algorithm to Optimize the High Fill-Factor Microlens Array Fabrication Using UV Proximity Printing Process”; by Hung et al.; Journal of Micromechanics and Microengineering, pp. 2388-2398; Nov. 14, 2005.

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