Automatic defect recognition of TFT array process using gray level co-occurrence matrix

Shih Wei Yang*, Chern Sheng Lin, Shir-Kuan Lin, Hsien Te Chiang

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

18 Scopus citations


This study proposed an automatic optical inspection (AOI) system for detection of thin-film transistor (TFT) array defects. gray level co-occurrence matrix (GLCM) and MATLAB regionprops function were used to calculate 53 TFT array defect features, which were inputted into the neural network to train the defect classifier. The images to be inspected were compared with a standard image first, in order to judge whether the TFT array samples have defects. For defective images of a TFT array, the proposed defect classifier can successfully recognize five kinds of defects in the process. According to the experimental results, the defect recognition rate of proposed system is verified to be 83.3%, which can replace manual inspection and reduce the risks of false inspections due to long duration manual work. Moreover, the proposed AOI system can improve testing efficiency and reduce manufacturing costs.

Original languageEnglish
Pages (from-to)2671-2676
Number of pages6
Issue number11
StatePublished - 1 Jan 2014


  • Automatic optical inspection system
  • Defect classifier
  • TFT array

Fingerprint Dive into the research topics of 'Automatic defect recognition of TFT array process using gray level co-occurrence matrix'. Together they form a unique fingerprint.

Cite this