# A Study on Defect Detection in Organic Light-Emitting Diode Cells Using Optimal Deep Learning
## 2 Related Research
### Study on Defect Detection in OLED Using Deep Learning  
Recent technological advancements have led to the use of deep learning in OLED displays for detecting minor defects. OLED panels contain microscopic cells, which limit the effectiveness of visual inspection. Although recent approaches use camera-based image analysis for defect detection, accurately classifying defect images without controlling for all environmental factors remains challenging. This trend is shifting towards utilizing artificial-intelligence-based image analysis techniques rather than camera-based image discrimination. In a previously reported study, we classified and studied the defects that occur during OLED production [9]. Our focus was on general and disjointed cell defects. A CNN-based model was developed to discriminate between cell defects. This involved collecting an OLED defect image dataset to train the deep learning model and evaluating its performance using test data. The research findings indicate that the developed model accurately detects defects in flexible OLEDs, leading to improved accuracy in defect detection and the reduced time and cost of defect detection through deep learning. In another study, [PERSON] presented a defect detection technique for the manufacturing process using deep learning [10]. This study used deep learning, specifically Convolutional Neural Networks (CNNs) such as Res-Net and Dense-Net, to identify different types of defects in X-ray images. The Dense-Net model demonstrated the highest performance, and data augmentation techniques such as rotation, translation, and flipping were used to increase the dataset size and improve the model performance. 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. The proposed method employs GAN based anomaly detection for real time and automatic data labeling to construct an efficient training dataset. Anomalies were detected in real time, and the resulting anomalous data were automatically labeled to create an effective training dataset. [PERSON] et al. used deep learning techniques for defect detection in patterned OLED panels with fine scale defects [12]. The proposed method uses defect inpainting and a multiscale Siamese neural network to detect minor defects in patterned OLED panels. This approach aims to detect defects using limited training data by leveraging few shot learning, which enables the detection of fine scale defects. [PERSON] et al. conducted research from the perspective of image defect detection, utilizing deep learning techniques for detecting defects in industrial products. He explored surface defect detection based on deep learning technology, identifying imbalanced samples and detecting defects in real time with limited sample sizes [13]. [PERSON] et al. suggested that automatic defect detection and classification in OLED panels are essential for quality management [14]. Therefore, this study designed an efficient and high performance system for defect classification by combining machine learning algorithms such as Support Vector Machines (SVMs), Random Forest, and K-Nearest Neighbors (K-NN). The approach involved using principal component analysis (PCA) to design potential features and automatically select the most effective features using Random Forest. A hierarchical structure for classifiers was proposed to achieve the efficient adjustment of the ratio between good and defective items in defect classification. The designed system achieved an accuracy of 94% for the binary classification. [PERSON] et al. employed deep convolutional neural networks (DCNNs) to segment small targets in LCD defects accurately [15].