Defect Inspection in Display Panel Using Concentrated Auto Encoder

Abstract

In this paper, concentrated auto encoder (CAE) is proposed for aligning photo spacer (PS) and for local inspection of PS. The CAE method has two characteristics. First, unaligned images can be moved to the same alignment position, which makes it possible to move the measured PS images to the same position in order to directly compare the images. Second, the characteristics of the abnormal PS are maintained even if the PS is aligned by the CAE method. The abnormal PS obtained through CAE has the same alignment as the reference PS and has its abnormal characteristics. The presence or absence of defects and the location of defects were identified without precisely measuring the height of the PS and critical dimension (CD). Also, alignment and defect inspection were performed simultaneously, which shortened the inspection time. Finally, inspection performance parameters and inspection time were analyzed to confirm the validity of the CAE method and were compared with the image similarity comparison methods used for defect inspection.

An efficient and facile method to develop defect-free OLED displays

“…To date, various approaches, such as machine learning [15], cascaded Mura detection [16] and independent component analysis [17], have been employed for their determination and removal from display panels. Recently, Ku proposed the concentrated auto encoder for the alignment and local inspection of the photo spacer [18]. In this work, the presence or absence of defects and their location were easily monitored by measuring the height of the photo spacer and critical dimension.…”