Applying Incremental Learning-based SVM to Gate-Electrode Mask Defect Classification for the Inline Inspection of TFT-LCD Array Engineering

• Main Authors: You-Jun Lin, 林佑駿
• Other Authors: 劉益宏
• Format: Others
• Language: zh-TW
• Published: 2008
• Online Access: http://ndltd.ncl.edu.tw/handle/29404148408945158003

碩士 === 中原大學 === 機械工程研究所 === 96 === For current TFT-LCD manufacturer, one of the most important goals is to enhance the yield rate and reduce the production cost. To enhance the yield rate, most companies have set up the inspection departments to manually perform the task of defect classification. A fully automatic inspection system is required in order to reduce the effects due to human factors and speed up the defect classification. However, the variation of defects is very large. That is, the classifier trained with the defect data collected with a specific period can not provide a good classification accuracy for those do not included in the training phase. Therefore, it is necessary to develop an online re-training model. As a classifier, support vector machine (SVM) has shown to be superior to the multi-layer neural networks trained by principle of empirical risk minimization. The learning strategy of SVM is based on the principle of structural risk minimization. Therefore, SVM can provide good generalization performance for unseen defect data. However, as mentioned above, the variation of defects is large. It can be expected that SVM will still suffer from the problem that some unseen defects would be misclassified. Therefore, how to develop an online training algorithm for SVM would be the key issue for achieving high TFT-array-process defect classification rate. This thesis focuses on this. In this thesis, an incremental learning based support vector machine (IL-SVM) is proposed to deal with this problem. IL-SVM is able to combine the existing support vectors and the newly coming data that are misclassified to re-train an optimal separating hyperplane (OSH). This method can not only reduce the number of training data required, but also reduce the time for re-training the SVM. What is the most important, IL-SVM can not only preserve the information of original training data, but also adapt to the newly coming data, thus improving the classification rate. For achieving the best cross-validation rate, the radial basis function (RBF) is adopted as the kernel function for SVM. This study aims at recognizing the types of the defect images captured in the photolithography process of gate-electrode engineering in TFT array process. The defects include “scratch”, “connection of gate electrode and capacity storage”, “abnormal resist coating”, and “foreign object”. They are commonly-seen defects in TFT array process, and are critical to TFT panels. The goals of real-time equipment diagnosis and maintenance can be achieved if the types of the defects can be timely recognized because each kind of defect has each cause of generation. As a result, the yield rate can be improved. In additional to the classifier design, this paper also discusses the feature selection problem. To obtain better representation of a defective image, the principal component analysis (PCA) algorithm is used to transform the input vector into a new one which has a lower dimension. The experiments are conducted on a set of real defect pictures provided by a TFT-LCD manufacturer. Experimental results show that the proposed IL-SVM is able to achieve a high defect recognition rate of over 95%. The results also indicate that PCA can further improve the classification accuracy. Keywords: thin film transistor liquid crystal display (TFT-LCD), incremental learning, support vector machine (SVM), defect classification, principal component analysis.