Paper Info
Title
How particle detector can aid visual inspection for defect detection of TFT-LCD manufacturing
Abstract
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.
Year
DOI
Venue
2020
10.1145/3410530.3414596
UbiComp/ISWC '20: 2020 ACM International Joint Conference on Pervasive and Ubiquitous Computing and 2020 ACM International Symposium on Wearable Computers Virtual Event Mexico September, 2020
DocType
ISBN
Citations 
Conference
978-1-4503-8076-8
0
PageRank 
References 
Authors
0.34
0
2
Name
Order
Citations
PageRank
Marzieh Khakifirooz133.39
Mahdi Fathi224.07