WaferSegClassNet - A light-weight network for classification and segmentation of semiconductor wafer defects
As the integration density and design intricacy of semiconductor wafers increase, the magnitude and complexity of defects in them are also on the rise. Since the manual inspection of wafer defects is costly, an automated artificial intelligence (AI) based computer-vision approach is highly desired....
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| Veröffentlicht in: | Computers in industry Jg. 142; S. 103720 |
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| Format: | Journal Article |
| Sprache: | Englisch |
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Elsevier B.V
01.11.2022
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| ISSN: | 0166-3615, 1872-6194 |
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| Abstract | As the integration density and design intricacy of semiconductor wafers increase, the magnitude and complexity of defects in them are also on the rise. Since the manual inspection of wafer defects is costly, an automated artificial intelligence (AI) based computer-vision approach is highly desired. The previous works on defect analysis have several limitations, such as low accuracy and the need for separate models for classification and segmentation. For analyzing mixed-type defects, some previous works require separately training one model for each defect type, which is non-scalable. In this paper, we present WaferSegClassNet (WSCN), a novel network based on encoder-decoder architecture. WSCN performs simultaneous classification and segmentation of both single and mixed-type wafer defects. WSCN uses a “shared encoder” for classification, and segmentation, which allows training WSCN end-to-end. We use N-pair contrastive loss to first pretrain the encoder and then use BCE-Dice loss for segmentation, and categorical cross-entropy loss for classification. Use of N-pair contrastive loss helps in better embedding representation in the latent dimension of wafer maps. WSCN has a model size of only 0.51MB and performs only 0.2 M FLOPS. Thus, it is much lighter than other state-of-the-art models. Also, it requires only 150 epochs for convergence, compared to 4000 epochs needed by a previous work. We evaluate our model on the MixedWM38 dataset, which has 38,015 images. WSCN achieves an average classification accuracy of 98.2% and a dice coefficient of 0.9999. We are the first to show segmentation results on the MixedWM38 dataset. The source code can be obtained from https://github.com/ckmvigil/WaferSegClassNet.
•Semiconductor Wafer Defect classification and segmentation.•Convolution neural network with encoder-decoder architecture.•A single network which performs both classification and segmentation.•Extremely low model size and computation overhead.•Results on MixedWM38 dataset with 38,015 images. |
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| AbstractList | As the integration density and design intricacy of semiconductor wafers increase, the magnitude and complexity of defects in them are also on the rise. Since the manual inspection of wafer defects is costly, an automated artificial intelligence (AI) based computer-vision approach is highly desired. The previous works on defect analysis have several limitations, such as low accuracy and the need for separate models for classification and segmentation. For analyzing mixed-type defects, some previous works require separately training one model for each defect type, which is non-scalable. In this paper, we present WaferSegClassNet (WSCN), a novel network based on encoder-decoder architecture. WSCN performs simultaneous classification and segmentation of both single and mixed-type wafer defects. WSCN uses a “shared encoder” for classification, and segmentation, which allows training WSCN end-to-end. We use N-pair contrastive loss to first pretrain the encoder and then use BCE-Dice loss for segmentation, and categorical cross-entropy loss for classification. Use of N-pair contrastive loss helps in better embedding representation in the latent dimension of wafer maps. WSCN has a model size of only 0.51MB and performs only 0.2 M FLOPS. Thus, it is much lighter than other state-of-the-art models. Also, it requires only 150 epochs for convergence, compared to 4000 epochs needed by a previous work. We evaluate our model on the MixedWM38 dataset, which has 38,015 images. WSCN achieves an average classification accuracy of 98.2% and a dice coefficient of 0.9999. We are the first to show segmentation results on the MixedWM38 dataset. The source code can be obtained from https://github.com/ckmvigil/WaferSegClassNet.
•Semiconductor Wafer Defect classification and segmentation.•Convolution neural network with encoder-decoder architecture.•A single network which performs both classification and segmentation.•Extremely low model size and computation overhead.•Results on MixedWM38 dataset with 38,015 images. |
| ArticleNumber | 103720 |
| Author | Nag, Subhrajit Makwana, Dhruv Mohan, C.Krishna R, Sai Chandra Teja Mittal, Sparsh |
| Author_xml | – sequence: 1 givenname: Subhrajit surname: Nag fullname: Nag, Subhrajit organization: IIT Hyderabad, India – sequence: 2 givenname: Dhruv surname: Makwana fullname: Makwana, Dhruv organization: CKMVigil Pvt Ltd, India – sequence: 3 givenname: Sai Chandra Teja surname: R fullname: R, Sai Chandra Teja email: saichandrateja@ckmvigil.in organization: CKMVigil Pvt Ltd, India – sequence: 4 givenname: Sparsh surname: Mittal fullname: Mittal, Sparsh organization: IIT Roorkee, India – sequence: 5 givenname: C.Krishna surname: Mohan fullname: Mohan, C.Krishna organization: IIT Hyderabad, India |
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| Cites_doi | 10.1016/j.patrec.2018.12.013 10.1109/TPAMI.2017.2699184 10.1080/00401706.1997.10485116 10.3390/en11051221 10.1016/j.ijleo.2019.163038 10.1109/TSM.2018.2795466 10.1109/TPAMI.2016.2644615 10.1186/s12864-019-6413-7 10.1109/TSM.2018.2825482 10.1080/07408170600733236 10.1109/TSM.2020.3020985 10.1002/int.22386 10.1109/TSM.2018.2841416 10.1109/TSM.2008.2005375 10.1109/5.726791 10.1016/S0026-2714(98)00127-9 10.1109/TSM.2018.2806931 10.1109/TSM.2019.2897690 |
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| Keywords | Semiconductor wafer defect analysis Encoder-decoder architecture Convolution neural network Image Segmentation Image classification |
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| SubjectTerms | Convolution neural network Encoder-decoder architecture Image classification Image Segmentation Semiconductor wafer defect analysis |
| Title | WaferSegClassNet - A light-weight network for classification and segmentation of semiconductor wafer defects |
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