A Neural-Network Approach for Defect Recognition in TFT-LCD Photolithography Process

Since the advent of high qualification and tiny technology, yield control in the photolithography process has played an important role in the manufacture of thin-film transistor-liquid crystal displays (TFT-LCDs). Through an auto optic inspection (AOI), defect points from the panels are collected, and the defect images are generated after the photolithography process. The defect images are usually identified by experienced engineers or operators. Evidently, human identification may produce potential misjudgments and cause time loss. This study therefore proposes a neural-network approach for defect recognition in the TFT-LCD photolithography process. There were four neural-network methods adopted for this purpose, namely, backpropagation, radial basis function, learning vector quantization 1, and learning vector quantization 2. A comparison of the performance of these four types of neural-networks was illustrated. The results showed that the proposed approach can effectively recognize the defect images in the photolithography process.