Wafer bin map inspection based on DenseNet

Yu, Naigong; Qiao, Xu; Wang, Hong-lu; Lin, Jia Chun

doi:10.1007/s11771-021-4778-7

Cited by 7 publications

(2 citation statements)

References 38 publications

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“…The AP is the area proportion of the shaded part in the P-R curve, which can be calculated by Eq. (11). The mAP is the mean value of the detection precision across all classes, which is employed to evaluate the overall capability of the detection models.…”

Section: Evaluation Indexmentioning

confidence: 99%

“…With the emergence of artificial intelligence and big data technologies, a series of deep learning methods represented by Chengdi Xiao xcd0719@ncu.edu.cn 1 College of Advanced Manufacturing, Nanchang University, Nanchang, 330031, China 2 R & D Center, Shanghai Highly Electric Co., Ltd., Shanghai, 201206, China convolutional neural network (CNN) have been introduced into the field of target detection, which can refine the features from shallow layers into more abstract deep layers and adapt different detection scenarios with excellent robustness [11,12].…”

Section: Introducingmentioning

confidence: 99%

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A Real-time and Efficient Surface Defect Detection Method Based on YOLOv4

Liu

Cui

Xiao

2022

Preprint

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In order to achieve a better balance between accuracy and speed with limited storage and computing resources in the field of industrial defect detection, a lightweight and fast detection framework Mixed YOLOv4-LITE series is proposed based on You Only Look Once (YOLOv4) in this paper. To reduce the size of model, MobileNet series (MobileNetv1, MobileNetv2, MobileNetv3) and depthwise separable convolutions are employed in the modified network architecture to replace the backbone network CSPdarknet53 and traditional convolution in the neck and head of YOLOv4, respectively. Moreover, we combine the Mosic data enhancement method to enrich the dataset. In the training stage, Transfer Learning is used to accelerate the convergence of network, in which pseudo-convergence is precluded as much as possible by adjusting the learning rate of the cosine annealing scheduler. Finally, we evaluate the proposed methods on both public defect datasets with different types and scales, namely NEU-DET and PCB-DET. On NEU-DET, Mixed YOLOv4-LITEv1, which can detect at a rate of 88 FPS on a single GPU while maintaining the accuracy, achieves an improvement of 214% in detection speed. And Mixed YOLOv4-LITEv3 realizes an outstanding maximum improvement of 200% in detection speed while only losing a mean average precision (mAP) value of 0.11% on PCB-DET. Furthermore, the sizes of our proposed series models are only about one-fifth of the original YOLOv4 model. The extensive test results indicate that our work can provide an efficient scheme with low deployment cost for surface defect detection at different scales in multiple scenarios, which can meet the needs of practical industrial applications.

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Section: Evaluation Indexmentioning

confidence: 99%

Section: Introducingmentioning

confidence: 99%

A Real-time and Efficient Surface Defect Detection Method Based on YOLOv4

Liu

Cui

Xiao

2022

Preprint

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Sparse deep encoded features with enhanced sinogramic red deer optimization for fault inspection in wafer maps

Altantawy,

Yakout

2024

J Intell Manuf

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Due to the complexity and dynamics of the semiconductor manufacturing processes, wafer bin maps (WBM) present various defect patterns caused by various process faults. The defect type detection on wafer maps provides information about the process and equipment in which the defect occurred. Recently, automatic inspection has played a vital role in meeting the high-throughput demand, especially with deep convolutional neural networks (DCNN) which shows promising efficiency. At the same time, the need for a large amount of labeled and balanced datasets limits the performance of such approaches. In addition, complex DCNN in recognition tasks can provide redundant features that cause overfitting and reduce interpretability. In this paper, a new hybrid deep model for wafer map fault detection to get over these challenges is proposed. Firstly, a new convolutional autoencoder (CAE) is employed as a synthetization model to fix the high imbalance problem of the dataset. Secondly, for efficient dimensionality reduction, an embedding procedure is applied to the synthesized maps to get sparse encoded wafer maps by reinforcing a sparsity regularization in an encoder-decoder network to form a sparsity-boosted autoencoder (SBAE). The sparse embedding of wafer maps guarantees more discriminative features with 50% reduction in spatial size compared to the original wafer maps. Then, the 2D encoded sparse maps are converted to 1D sinograms to be fed later into another aggressive feature reduction stage using a new modified red deer algorithm with a new tinkering strategy. The resultant feature pool is reduced to ~ 25 1D feature bases, i.e., ~ 1.5% of the initial size of the 2D wafer maps. Finally, for the prediction stage, a simple 1DCNN model is introduced. The proposed inspection model is tested via different experiments on real-world wafer map dataset (WM-811K). Compared to state-of-the-art techniques, the proposed model outperforms their performance even with small-sized 1D feature pool. The average testing accuracy are 98.77% and 98.8% for 9 and 8 types of faults, respectively.

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