| dc.description.abstract | With the development of technology, LCD(liquid crystal display) has become the core of displays nowadays. The quality of LCD is one of the targets making customers to purchase. To make sure the quality of LCD, defect detection plays an important role in this field.
Nowadays, most defect detections made by human eyes often causes failure in defect detections and inconsistency of quality. Among these defects, MURA defection is most hardly to be detected. MURA defection result from the um-uniform brightness which makes traces on LCD. Due to the unapparent defection, it often takes lots of time to detect but often out of judgement. To solve these defections above, this research designs a set of detection system to replace human eyes.
On the image of MURA detection, it’s hard to use normal threshold to detect MURA due to the brightness um-uniform of background. For this reason, this research intends to estimate background of LCD. With this method, we hope to eliminate the effect caused by the background to reveal MURA detection. Singular value decomposition (SVD) expands original image into several based images－these based images are composed of eigenvalue and responded eigenvector. Therefore, the bigger eigenvalue is, the more important feature the original image is. The um-uniform of background is major component, so we take the biggest eigenvalue and responded eigenvector representing major element — the background. Actually, from this experiment we know that the background of the samples is more complex, so we must to separate several blocks to achieve detection of goal. For above reasons, we provide an optimum method to overcome the problem and add the value of SEMU (which is an ergonomics experiment completed by Semiconductor Equipment Materials International, SEMI) to identify real Mura.
In software, we complete an interface of automatic system of detection Mura which can provide related information of defect. | en_US |