Deep-Learning-Based Defect Evaluation of Mono-Like Cast Silicon Wafers

Fu, Yongzhong; Li, Xiufeng; Ma, Xiaolong

doi:10.3390/photonics8100426

Cited by 4 publications

(2 citation statements)

References 19 publications

(19 reference statements)

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“…Experiment results showed that the applied CNN network system outperformed the other two applied networks. Jianbo Yu [9] implemented enhanced stacked denoising autoencoder (ESDAE) on wafer dataset (WM-811K) for wafer map pattern recognition. The proposed method was compared with commonly used recognizers like SVM, BPN and DPN.…”

Section: Literature Reviewmentioning

confidence: 99%

High Accuracy Swin Transformers for Imagebased Wafer Map Defect Detection

Nafi¹,

Haque²,

Farhan³

et al. 2022

IJEM

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A wafer map depicts the location of each die on the wafer and indicates whether it is a Product, Secondary Silicon, or Reject. Detecting defects in Wafer Maps is crucial in order to ensure the integrity of the chips processed in the wafer, as any defect can cause anomalies thus decreasing the overall yield. With the current advances in anomaly detection using various Computer Vision Techniques, Transformer Architecture based Vision models are a prime candidate for identifying wafer defects. In this paper, the performance of Four such Transformer based models -BEiT (BERT Pre-Training of Image Transformers), FNet (Fourier Network), ViT (Vision Transformer) and Swin Transformer (Shifted Window based Transformer) in wafer map defect classification are discussed. Each of these models were individually trained, tested and evaluated with the "MixedWM38" dataset obtained from the online platform, Kaggle. During evaluation, it has been observed that the overall accuracy of the Swin Transformer Network algorithm is the highest, at 97.47%, followed closely by Vision Transformer at 96.77%. The average Recall of Swin Transformer is also 97.54%, which indicates an extremely low encounter of false negatives (24600 ppm) in contrast to true positives, making it less likely to expose defective products in the market.

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Section: Literature Reviewmentioning

confidence: 99%

High Accuracy Swin Transformers for Imagebased Wafer Map Defect Detection

Nafi¹,

Haque²,

Farhan³

et al. 2022

IJEM

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“…Additionally, the use of computer vision in detecting defects in railway tracks has already been studied [16,17]. Machine learning methods have been successfully applied in order to detect and monitor surface defects of photovoltaic panels (PV) [18,19]. The improved MobilenetV1-YOLOv4 network has been successfully used to detect insulation defects and thereby improve the security of power lines [20].…”

Section: Introductionmentioning

confidence: 99%

Discovery and Classification of Defects on Facing Brick Specimens Using a Convolutional Neural Network

et al. 2023

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In recent years, visual automatic non-destructive testing using machine vision algorithms has been widely used in industry. This approach for detecting, classifying, and segmenting defects in building materials and structures can be effectively implemented using convolutional neural networks. Using intelligent systems in the initial stages of manufacturing can eliminate defective building materials, prevent the spread of defective products, and detect the cause of specific damage. In this article, the solution to the problem of building elements flaw detection using the computer vision method was considered. Using the YOLOv5s convolutional neural network for the detection and classification of various defects of the structure, the appearance of finished products of facing bricks that take place at the production stage is shown during technological processing, packaging, transportation, or storage. The algorithm allows for the detection of foreign inclusions, broken corners, cracks, and color unevenness, including the presence of rust spots. To train the detector, our own empirical database of images of facing brick samples was obtained. The set of training data for the neural network algorithm for discovering defects and classifying images was expanded by using our own augmentation algorithm. The results show that the developed YOLOv5s model has a high accuracy in solving the problems of defect detection: mAP0.50 = 87% and mAP0.50:0.95 = 72%. It should be noted that the use of synthetic data obtained by augmentation makes it possible to achieve a good generalizing ability from the algorithm, it has the potential to expand visual variability and practical applicability in various shooting conditions.

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