In TFT array process, the target defects would cause serious damages to the LCD panels.Īccording to the above analysis, it is known that the third sub-task is still an issue to be solved. If a test pattern is accepted by the SVDD ensemble, the pattern belongs to the target class. In their work the SVDD was extended to an SVDD ensemble for modeling the target defects. have also proposed a target defect identification system. For inline defect classification, the SVDD is not a good candidate. Although it is efficient for anomaly detection, it cannot be applied in a multi-class classification problem. SVDD is essentially a one-class classifier. However, it suffers from the out-of-sample problem. LLE is a manifold learning method to extract nonlinear features from a pattern. have recently proposed a system to deal with the problem of inline defect detection, which was developed based on the locally linear embedding (LLE) method and the support vector data description (SVDD). Defect classification plays a critical role in production-equipment diagnosis because different defects have different causes. Defect detection refers to judging whether an image contains a defect or not, and target defect identification means determining whether the defect detected is crucial to the product yield. Inline defect inspection involves three sub-tasks: defect detection, target defect identification, and classification. The inline defects vary greatly, and their sizes are too small to be observed, making the problem of inline defect inspection intractable. In fact, most mura defects are caused by the inline defects of the TFT array process. Once a mura defect is found in a panel, this panel must be discarded if not repairable, which raises the production costs greatly. Mura is a serious kind of defect and needs to be detected in the cell process. In recent years, there has been a large body of work regarding the so-called mura-defect detection, e.g. TFT-LCD manufacture consists of three processes, namely the TFT array process, the cell process, and the module assembly process. Accordingly, automatic optical inspection (AOI) has been suggested as the most efficient way to detect defects. However, in current practice, this task still relies heavily on human observers, which is not only time consuming, but also prompt to be unreliable. Such a change usually results in various defects, which decrease the yield rate significantly, therefore, defect inspection plays a key role in TFT-LCD manufacture.
To make large-sized TFT-LCDs, the original manufacturing process needs to be changed to meet the requirements. With the increase in demand, the sizes of the TFT-LCDs have been getting larger. Over the past decade the TFT-LCD has been a popular flat panel display choice. More importantly, the inspection time is less than 1 s per input image. Results, based on real images provided by a LCD manufacturer in Taiwan, indicate that the KPCA-based defect inspection scheme is able to achieve a defect detection rate of over 99% and a high defect classification rate of over 96% when the imbalanced support vector machine (ISVM) with 2-norm soft margin is employed as the classifier. The inspection scheme can not only detect the defects from the images captured from the surface of LCD panels, but also recognize the types of the detected defects automatically.
This paper presents a novel inspection scheme based on kernel principal component analysis (KPCA) algorithm, which is a nonlinear version of the well-known PCA algorithm. Defect inspection in TFT array process is therefore considered a difficult task. Previously, most focus was put on the problems of macro-scale Mura-defect detection in cell process, but it has recently been found that the defects which substantially influence the yield rate of LCD panels are actually those in the TFT array process, which is the first process in TFT-LCD manufacturing. Defect inspection plays a critical role in thin film transistor liquid crystal display (TFT-LCD) manufacture, and has received much attention in the field of automatic optical inspection (AOI).
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