abstract
- © 2020 ACM.Traditional defect classification of TFT-LCD array processing leaned on human decision-maker in which visual inspection used to categorize defects and consequently identify the rout-causes of defects. In practice, the main sources of defects in the TFT-LCD array process are particles. Due to the huge size of the machinery and production tools in the TFT-LCD array process, the sensor allocation for particle detection plays a critical role in the inadequacy and quality of sensor data. Therefore, where the adequacy and efficiency of human performance depend on human factors, emotion, and level of attention, this study aims to design a semi-automatic defect detection and classification method based on information capture by particle detector sensors to reduce the cognitive load devaluation and proceed with the process of defect classification.