P‐2.2: Anomaly Detection Based on Generative Adversarial Network in the Manufacturing Process of LCD/OLED Display Panels

Zhan, Dongxu; Zhang, Shengsen

doi:10.1002/sdtp.14521

Cited by 3 publications

(2 citation statements)

References 6 publications

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“…The proposed system achieved an F1-score of 80%, indicating its potential as a valuable technology for defect detection in metal manufacturing processes. A study was conducted on anomaly detection in LCD/OLED display panel manufacturing using Generative Adversarial Networks [11]. The detection of defects in display panels requires the identification and categorization of anomalies based on specific criteria.…”

Section: Study On Defect Detection In Oled Using Deep Learningmentioning

confidence: 99%

A Study on Defect Detection in Organic Light-Emitting Diode Cells Using Optimal Deep Learning

Chung,

Kim,

Kim

et al. 2023

Applied Sciences

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In this study, we applied an optimal deep learning algorithm to detect defects in OLED cells. This study aims to enhance the yield of OLEDs by reducing the number of defective products through defect detection in OLED cells. Defects in OLED cells can arise owing to various factors, but dark spots are predominantly identified and studied. Therefore, actual dark spot images were required for this study. However, obtaining real data is challenging because of security concerns in the OLED industry. Therefore, a Solver program utilizing the finite element method (FEM) was employed to generate 2000 virtual dark spot images. The generated images were categorized into two groups: initial images of dark spots and images obtained after 10,000 h. The pixel values of these dark spot images were adjusted for efficient recognition and analysis. Furthermore, CNN, ResNet-50, and VGG-16 were implemented to apply the optimal deep learning algorithms. The results showed that the VGG-16 algorithm performed the best. A defect detection model was created based on the performance metrics of the deep learning algorithms. The model was trained using 1300 dark spot images and validated using 600 dark spot images. The validation results indicated an accuracy of 0.988 and a loss value of 0.026. A defect detection model that utilizes the VGG-16 algorithm was considered suitable for distinguishing dark spot images. To test the defect detection model, 100 images of dark spots were used. The experimental results indicated an accuracy of 89%. The images were classified as acceptable or defective based on the threshold values. By applying the VGG-16 deep learning algorithm to the defect detection model, we can enhance the yield of OLED products, reduce production costs, and contribute significantly to the advancement of OLED display manufacturing technology.

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Section: Study On Defect Detection In Oled Using Deep Learningmentioning

confidence: 99%

A Study on Defect Detection in Organic Light-Emitting Diode Cells Using Optimal Deep Learning

Chung,

Kim,

Kim

et al. 2023

Applied Sciences

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“…This paper reviewed the conditional gan-based detection with the generative adversarial network (AnoGAN) and the wasserstein gan-based fast AnoGAN (f-AnoGAN) models [4][5][6]. There are many examples of studying anomaly detection methodology in display manufacturing [7][8]. After the detection of outliers, it is necessary to analyze to detect cause factors in order to improve the process.…”

Section: Related Workmentioning

confidence: 99%

P‐102: Explainable AI Approach for MOD Outliers in FAB to MOD Process

Park,

Choi,

Yim

et al. 2023

Symp Digest of Tech Papers

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Display panels manufactured in the FAB process are assembled with various films and components in the module (MOD) process, and their final quality is a result of accumulated the quality of hundreds of preceding processes. Due to these limitations, it is hard to analyze the cause of MOD's outliers relevant to the FAB process and much time and money are consumed to maintain the cause process to eliminate MOD outliers. Therefore, we propose a new explainable AI (XAI) approach to detect and improve the module process's outliers by finding the correlation between FAB and MOD in this paper. The proposed approach was verified for MOD outlier cases in the real world's OLED development and manufacturing process. As a result, it confirmed 96.8% of the average accuracy for classifications. This approach presents the FAB factor that caused MOD's outliers based on the correlation between the MOD and the FAB.

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Machine learning-based non-destructive method for identifying defect causes in OLED displays to enhance productivity

Han,

Jeong,

Chun

et al. 2024

J Intell Manuf

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P‐2.2: Anomaly Detection Based on Generative Adversarial Network in the Manufacturing Process of LCD/OLED Display Panels

Cited by 3 publications

References 6 publications

A Study on Defect Detection in Organic Light-Emitting Diode Cells Using Optimal Deep Learning

A Study on Defect Detection in Organic Light-Emitting Diode Cells Using Optimal Deep Learning

P‐102: Explainable AI Approach for MOD Outliers in FAB to MOD Process

Machine learning-based non-destructive method for identifying defect causes in OLED displays to enhance productivity

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