P‐236: Late‐News Poster: Hard defect detection and classification for display panel products

Abstract

Hard defect of display panel such as crack or chipping is one of the most critical defects that have fatal impacts on the product. Conventional deep‐learning‐based methods have difficulties in equipment control in practical point of view due to the vastness of training data and numerous parameters. Consequently, more simple and robust machine‐learning models jointly with traditional image processing algorithms with small number of parameters are increasingly required for the maintenance of inspection equipment. To resolve the issue, we proposed a novel image processing and decision tree classification algorithm that can be operated using smaller number of parameters than deep‐learning method. Firstly, panel image is fed into proposed image processing algorithm and converted into feature map to highlight the hard defect. After binary thresholding of the image, remaining pixels are specified as bounding box. Subsequently, features are extracted in the bounding box followed by classification into normal/defective panel. Additional RANSAC‐based outer edge inspection algorithm is processed. We conducted an experimental study using manufacturing data, and showed successful performance in hard defect detection and classification.