Marzieh Khakifirooz
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.
- Yi-Gang Cen, Rui-Zhen Zhao, Li-Hui Cen, Li-Hong Cui, Zhen-Jiang Miao, and Zhe Wei. 2015. Defect inspection for TFT-LCD images based on the low-rank matrix reconstruction. Neurocomputing 149 (2015), 1206--1215.Google Scholar
Cross Ref
- Kuen-Suan Chen, CH Wang, and HT Chen. 2006. A MAIC approach to TFT-LCD panel quality improvement. Microelectronics Reliability 46, 7 (2006), 1189--1198.Google ScholarCross Ref
- Li-Fei Chen, Chao-Ton Su, and Meng-Heng Chen. 2009. A neural-network approach for defect recognition in TFT-LCD photolithography process. IEEE transactions on electronics packaging manufacturing 32, 1 (2009), 1--8.Google ScholarCross Ref
- LU Fangbo, Feng Yao, DENG Biaohua, Kai Chen, and SHEN Yafei. 2020. Background suppression method and detecting device in automatic optical detection of display panel. US Patent 10,621,701.Google Scholar
- Hongseok Jang, Seonghwan Jeon, Hyoungseok So, and Seungyoung So. 2015. Properties of different particle size of recycled TFT-LCD waste glass powder as a cement concrete binder. International Journal of Precision Engineering and Manufacturing 16, 12 (2015), 2591--2597.Google ScholarCross Ref
- Myeongso Kim, Minyoung Lee, Minjeong An, and Hongchul Lee. 2019. Effective automatic defect classification process based on CNN with stacking ensemble model for TFT-LCD panel. Journal of Intelligent Manufacturing (2019), 1--10.Google Scholar
- Eryun Liu, Kangping Chen, Zhiyu Xiang, and Jun Zhang. 2019. Conductive particle detection via deep learning for ACF bonding in TFT-LCD manufacturing. Journal of Intelligent Manufacturing (2019), 1--13.Google Scholar
- Yi-Hung Liu, Yan-Chen Liu, and Yen-Zen Chen. 2011. High-speed inline defect detection for TFT-LCD array process using a novel support vector data description. Expert Systems with Applications 38, 5 (2011), 6222--6231.Google ScholarDigital Library
- Chi-Jie Lu and Du-Ming Tsai. 2008. Independent component analysis-based defect detection in patterned liquid crystal display surfaces. Image and Vision Computing 26, 7 (2008), 955--970.Google ScholarDigital Library
- Cheng-Kuo Tsai, Ruey-an Doong, and Huan-Yi Hung. 2019. Sustainable valorization of mesoporous aluminosilicate composite from display panel glasses waste for adsorption of heavy metal ions. Science of the total environment 673 (2019), 337--346.Google Scholar
- D-M Tsai, S-T Chuang, and Y-H Tseng. 2007. One-dimensional-based automatic defect inspection of multiple patterned TFT-LCD panels using Fourier image reconstruction. International journal of production research 45, 6 (2007), 1297--1321.Google ScholarCross Ref
- Yuanyuan Wang, Ling Ma, Mingyuan Jiu, and Huiqin Jiang. 2020. Detection of Conductive Particles in TFT-LCD Circuit using Generative Adversarial Networks. IEEE Access (2020).Google ScholarCross Ref
Index Terms
- How particle detector can aid visual inspection for defect detection of TFT-LCD manufacturing
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