The Defect Classification of TFT-LCD Array Photolithography Process via Using Back-Propagation Neural Network

Shih Feng Chen; Chin Chih Lai

doi:10.4028/www.scientific.net/AMM.378.340

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vol. 378The Defect Classification of TFT-LCD Array...

The Defect Classification of TFT-LCD Array Photolithography Process via Using Back-Propagation Neural Network

Abstract:

This research is conducted mainly by using the Auto Optical Inspection (AOI) in the fifth generation TFT-LCD factory. In the development of detect-classification system, we designed the back-propagation neural network which combined with Visual Basic as the interface and MATLAB as an image-processing tool. The system is able to determine and display the detected results. The defect classification mainly designed to detect and classify the following defects: the second layer of the photo resist residue (AS-Residue), the second layer of large-area photo resist residue (AS-BPADJ), and the third layer of photo resist residue (M2-residue) in the Array Photolithography Process. Finally, the result is shown the fact that without the complicated processing procedures, the four defects in the TFT-LCD Array Photo Process can be precisely and quickly classified by imaging processing and back-propagation neural network training. As result, it is feasible to reduce the costs and the risk of human judgments.

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Periodical:

Applied Mechanics and Materials (Volume 378)

Pages:

340-345

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.378.340

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Online since:

August 2013

Authors:

Shih Feng Chen, Chin Chih Lai

Keywords:

AOI, Back-Propagation Neural Network, Defect Classification, TFT-LCD Array Inspection

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