An Integrated LCD Quality Assessment Flow Based on the Integration of Image Processing, Ergonomics, and Machine Learning

In mass production, good and stable quality control and inspections are crucial. However, up to recent, LCD inspection process still highly relies on manually visual inspection from experienced works and could possibly be biased due to subjective judgement. Recently, automated optical inspection (AOI) has been adopted to LCD inspection for years, but defect classification and overall quality assessment still relies on human inspectors, which is inevitably biased by the physical and psychological conditions and a more objective manner and a rational flow should be developed based on computerized inspections. In particular, LCD inspections should accomplish both local defect classification and global mura defect quantification simultaneously and this represents a bottleneck for preventing LCD inspection automation. Thus, this work focuses on proposing a rational flow for addressing the above needs from classification and to quantify the possible LCD defects by using convolutional neural network and image processing to realize an objective defect classification based on both CNN and image thresholding. In addition, a novel quality evaluation index has also been proposed by adding the information of defected area, defected gradient, and average brightness of defected zone, to improve the existed SEMI index based on ergodic experiments. In summary, the flow presented in this work has achieved better and objective evaluations than that performed by human inspection. In the future, based on the flow outlined, it is expected that a more substantial defect classification and evaluation model suitable could be realized. This would benefit to LCD industries.