Polycrystalline silicon wafer defect segmentation based on deep convolutional neural networks

Han, Hu; Gao, Chenqiang; Zhao, Yiqiang; Liao, Shisha; Lin, Tong; Li, Xindou

doi:10.1016/j.patrec.2018.12.013

Cited by 65 publications

(32 citation statements)

References 25 publications

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“…In reference to [18], predicted results are compared with ground truth images using five metrics: mean-Intersection-over-Union (m-IOU), accuracy, recall, precision and F-measure. The IOU, accuracy, recall, precision and F-measure are computed by Equations (13), (14), (15), (16) and (17) Table.2.…”

Section: Experiments Results and Analysis A Evaluation Metricmentioning

confidence: 99%

“…In this paper, we compare the multi attention u-net with Tsai's method [9], SEF method [11] and Han's u-net [15]. We evaluate the segmentation performance of proposed method and state of the art methods using k-Fold cross validation.…”

Section: Segmentation Resultsmentioning

confidence: 99%

“…Researcher in the field of industrial defect inspection also adopts the power of DCNN to solve the problems of segmentation and classification of industrial defects. Recently, Han et al [15] added a region proposal network (RPN) into U-net [16] and used dilated convolutions to segment polycrystalline silicon wafer defects. The method gives reasonable results but combining RPN and dilated convolution makes the detection slower.…”

Section: Introductionmentioning

confidence: 99%

“…respectively as follows IOU = TP TP + FP + FN (13) Accuracy = TP + TN TP + FP + FN + TN (14)Recall = TP TP + FP(15) …”

mentioning

confidence: 99%

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Defects Inspection in Polycrystalline Solar Cells Electroluminescence Images Using Deep Learning

Rahman

Chen

2020

IEEE Access

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Solar cells defects inspection plays an important role to ensure the efficiency and lifespan of photovoltaic modules. However, it is still an arduous task because of the diverse attributes of electroluminescence images, such as indiscriminative complex background with extremely unbalanced defects and various types of defects. In order to deal with these problems, this paper proposes a new precise and accurate defect inspection method for photovoltaic electroluminescence (EL) images. The proposed algorithm leverages the advantage of multi attention network to efficiently extract the most important features and neglect the nonessential features during training. Firstly, we designed a channel attention to exploit contextual representations and spatial attention to effectively suppress background noise. Secondly, we incorporate both attention networks into modified U-net architecture and named it multi attention U-net (MAU-net) to extract effective multiscale features for defects inspection. Finally, we propose a hybrid loss which combines focal loss and dice loss aiming to solve two problems: a) overcome the class imbalance problem, and b) allowing the network to train with irregular image labels for some complex defects. The proposed multi attention U-net is evaluated on real photovoltaic EL images datasets using 5-fold cross validation technique. Experimental results demonstrate that the proposed network can segment and detect various complex defects correctly. The proposed method achieved the mean intersection over-union (m-IOU) of 0.699 and F-measure of 0.799 which outperforms the previous methods.

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Section: Experiments Results and Analysis A Evaluation Metricmentioning

confidence: 99%

Section: Segmentation Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…respectively as follows IOU = TP TP + FP + FN (13) Accuracy = TP + TN TP + FP + FN + TN (14)Recall = TP TP + FP(15) …”

mentioning

confidence: 99%

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Defects Inspection in Polycrystalline Solar Cells Electroluminescence Images Using Deep Learning

Rahman

Chen

2020

IEEE Access

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“…In the wafer defect detection, CNN also achieved a good performance. Hui Han proposed a defect segmentation method for polycrystalline silicon wafer by means of the deep convolutional networks, which can segment various defects in silicon wafer by training with small amount of roughly marked defect images [22]. Nakazawa presented a method for wafer map defect detection by using CNN.…”

Section: Introductionmentioning

confidence: 99%

A Light-Weighted CNN Model for Wafer Structural Defect Detection

Chen

Han

et al. 2020

IEEE Access

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Silicon wafer is the raw material of semiconductor chip. It is important and challenging to research a fast and accurate method of identifying and classifying wafer structural defects. To this end, we present a novel detection method in terms of the convolution neural networks (CNN), which achieve more than 99% detection accuracy. Due to the wafer images are not available by open datasets, a set of imaging acquisition system is designed to capture wafer images. Digital image preprocessing technology is utilized to split a wafer image into thousands of silicon grain images. The proposed model, called WDD-Net, uses depthwise separable convolutions and global average pooling to reduce parameters and calculations, adopts multiple 1 * 1 standard convolutions to increase the network depth. Specifically, two types of CNN models, VGG-16 and MobileNet-v2, are adopted for comparative analysis. Using the aforementioned three models, the comparative experiments are implemented on data sets that consisting of more than ten thousand grain images. The experimental results show that compared with VGG-16 and MobileNet-v2, the detection speed of the WDD-Net is 105.6FPS, which is 5 times faster. The model size of the WDD-Net is 307KB, which is much smaller than the other two. Furthermore, the WDD-Net directly completes the data collection and defect detection process through the local computing equipment, which is suitable for edge computing. INDEX TERMS Image classification, neural networks, semiconductor manufacturing, machine learning.

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Machine Learning for Photovoltaic Applications

Dwivedi,

Hameiri

2024

Photovoltaic Solar Energy

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Polycrystalline silicon wafer defect segmentation based on deep convolutional neural networks

Cited by 65 publications

References 25 publications

Defects Inspection in Polycrystalline Solar Cells Electroluminescence Images Using Deep Learning

Defects Inspection in Polycrystalline Solar Cells Electroluminescence Images Using Deep Learning

A Light-Weighted CNN Model for Wafer Structural Defect Detection

Machine Learning for Photovoltaic Applications

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