{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,20]],"date-time":"2026-02-20T16:38:18Z","timestamp":1771605498965,"version":"3.50.1"},"reference-count":32,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2024,6,23]],"date-time":"2024-06-23T00:00:00Z","timestamp":1719100800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,6,23]],"date-time":"2024-06-23T00:00:00Z","timestamp":1719100800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"Sichuan Science and Technology Program","award":["2023NSFSC0497"],"award-info":[{"award-number":["2023NSFSC0497"]}]},{"name":"Sichuan Science and Technology Program","award":["2023YFG0056"],"award-info":[{"award-number":["2023YFG0056"]}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program","doi-asserted-by":"crossref","award":["2023YFB4707200"],"award-info":[{"award-number":["2023YFB4707200"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["52175031"],"award-info":[{"award-number":["52175031"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Intell Manuf"],"published-print":{"date-parts":[[2025,8]]},"DOI":"10.1007\/s10845-024-02441-z","type":"journal-article","created":{"date-parts":[[2024,6,23]],"date-time":"2024-06-23T14:02:05Z","timestamp":1719151325000},"page":"4039-4053","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["A chip inspection system based on a multiscale subarea attention network"],"prefix":"10.1007","volume":"36","author":[{"given":"Yun","family":"Hou","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hong","family":"Fan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ying","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-4694-0822","authenticated-orcid":false,"given":"Guangshuai","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,6,23]]},"reference":[{"key":"2441_CR1","doi-asserted-by":"crossref","unstructured":"Alom, M. Z., Hasan, M., Yakopcic, C., Taha, T. M., & Asari, V. K. (2018). Recurrent residual convolutional neural network based on U-Net (R2U-Net) for medical image segmentation. CoRR, abs\/1802.06955. http:\/\/arxiv.org\/abs\/1802.06955","DOI":"10.1109\/NAECON.2018.8556686"},{"key":"2441_CR2","doi-asserted-by":"crossref","unstructured":"Chen, L., Zhang, H., Xiao, J., Nie, L., Shao, J., Liu, W., & Chua, T.-S. (2017). SCA-CNN: Spatial and channel-wise attention in convolutional networks for image captioning. In 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 6298\u20136306).","DOI":"10.1109\/CVPR.2017.667"},{"key":"2441_CR3","doi-asserted-by":"publisher","unstructured":"Chen, X., Zhao, Z., Yu, F., Zhang, Y., & Duan, M. (2021). Conditional diffusion for interactive segmentation. In 2021 IEEE\/CVF International Conference on Computer Vision (ICCV) (pp. 7325\u20137334). https:\/\/doi.org\/10.1109\/ICCV48922.2021.00725","DOI":"10.1109\/ICCV48922.2021.00725"},{"key":"2441_CR4","doi-asserted-by":"publisher","unstructured":"Chen, X., Zhao, Z., Zhang, Y., Duan, M., Qi, D., & Zhao, H. (2022). FocalClick: Towards practical interactive image segmentation. In 2022 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 1290\u20131299). https:\/\/doi.org\/10.1109\/CVPR52688.2022.00136","DOI":"10.1109\/CVPR52688.2022.00136"},{"issue":"1","key":"2441_CR5","doi-asserted-by":"publisher","first-page":"269","DOI":"10.1007\/BF01386390","volume":"1","author":"EW Dijkstra","year":"1959","unstructured":"Dijkstra, E. W. (1959). A note on two problems in connexion with graphs. Numerische Mathematik, 1(1), 269\u2013271. https:\/\/doi.org\/10.1007\/BF01386390","journal-title":"Numerische Mathematik"},{"key":"2441_CR6","doi-asserted-by":"publisher","unstructured":"Fu, J., Liu, J., Tian, H., Li, Y., Bao, Y., Fang, Z., & Lu, H. (2019). Dual attention network for scene segmentation. In 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 3141\u20133149). https:\/\/doi.org\/10.1109\/CVPR.2019.00326","DOI":"10.1109\/CVPR.2019.00326"},{"key":"2441_CR7","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., & Sun, G. (2018). Squeeze-and-Excitation Networks. In 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (pp. 7132\u20137141).","DOI":"10.1109\/CVPR.2018.00745"},{"key":"2441_CR8","doi-asserted-by":"publisher","unstructured":"Jang, W.-D., & Kim, C.-S. (2019). Interactive image segmentation via backpropagating refinement scheme. In 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 5292\u20135301). https:\/\/doi.org\/10.1109\/CVPR.2019.00544","DOI":"10.1109\/CVPR.2019.00544"},{"key":"2441_CR9","doi-asserted-by":"crossref","unstructured":"Kim, T., Lee, H., & Kim, D. (2021). UACANet: Uncertainty augmented context attention for polyp segmentation. CoRR, abs\/2107.02368. https:\/\/arxiv.org\/abs\/2107.02368","DOI":"10.1145\/3474085.3475375"},{"key":"2441_CR10","doi-asserted-by":"crossref","unstructured":"Kirillov, A., Mintun, E., Ravi, N., Mao, H., Rolland, C., Gustafson, L., Xiao, T., Whitehead, S., Berg, A. C., Lo, W. Y., Doll\u00e1r, P., Girshick, R. (2023). Segment anything.","DOI":"10.1109\/ICCV51070.2023.00371"},{"key":"2441_CR11","doi-asserted-by":"publisher","unstructured":"Lin, Z., Zhang, Z., Chen, L.-Z., Cheng, M.-M., & Lu, S.-P. (2020). Interactive image segmentation with first click attention. In 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 13336\u201313345). https:\/\/doi.org\/10.1109\/CVPR42600.2020.01335","DOI":"10.1109\/CVPR42600.2020.01335"},{"key":"2441_CR12","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TIM.2022.3154814","volume":"71","author":"Z Ling","year":"2022","unstructured":"Ling, Z., Zhang, A., Ma, D., Shi, Y., & Wen, H. (2022). Deep siamese semantic segmentation network for PCB welding defect detection. IEEE Transactions on Instrumentation and Measurement, 71, 1\u201311. https:\/\/doi.org\/10.1109\/TIM.2022.3154814","journal-title":"IEEE Transactions on Instrumentation and Measurement"},{"key":"2441_CR13","doi-asserted-by":"publisher","unstructured":"Maninis, K.-K., Caelles, S., Pont-Tuset, J., & Van Gool, L. (2018). Deep extreme cut: From extreme points to object segmentation. In 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (pp. 616\u2013625). https:\/\/doi.org\/10.1109\/CVPR.2018.00071","DOI":"10.1109\/CVPR.2018.00071"},{"key":"2441_CR14","unstructured":"Oktay, O., Schlemper, J., Folgoc, L. L., Lee, M. J., Heinrich, M. P., Misawa, K., McDonagh, S., Hammerla, N. Y., Kainz, B., Glocker, B., & Rueckert, D. (2018). Attention U-Net: Learning Where to Look for the Pancreas. ArXiv, abs\/1804.03999. https:\/\/api.semanticscholar.org\/CorpusID:4861068"},{"issue":"2","key":"2441_CR15","doi-asserted-by":"publisher","first-page":"147","DOI":"10.1080\/24725854.2021.2018528","volume":"55","author":"S Park","year":"2023","unstructured":"Park, S., Kim, K., & Kim, H. (2023). Prediction of highly imbalanced semiconductor chip-level defects using uncertainty-based adaptive margin learning. IISE Transactions, 55(2), 147\u2013155. https:\/\/doi.org\/10.1080\/24725854.2021.2018528","journal-title":"IISE Transactions"},{"key":"2441_CR16","first-page":"234","volume-title":"Medical image computing and computer-assisted intervention \u2013 MICCAI 2015","author":"O Ronneberger","year":"2015","unstructured":"Ronneberger, O., Fischer, P., & Brox, T. (2015). U-Net: Convolutional networks for biomedical image segmentation. In N. Navab, J. Hornegger, W. M. Wells, & A. F. Frangi (Eds.), Medical image computing and computer-assisted intervention \u2013 MICCAI 2015 (pp. 234\u2013241). Springer International Publishing."},{"key":"2441_CR17","doi-asserted-by":"publisher","unstructured":"Sofiiuk, K., Petrov, I., Barinova, O., & Konushin, A. (2020). F-BRS: Rethinking backpropagating refinement for interactive segmentation. In 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 8620\u20138629). https:\/\/doi.org\/10.1109\/CVPR42600.2020.00865","DOI":"10.1109\/CVPR42600.2020.00865"},{"key":"2441_CR18","doi-asserted-by":"publisher","unstructured":"Sofiiuk, K., Petrov, I. A., & Konushin, A. (2022). Reviving iterative training with mask guidance for interactive segmentation. In 2022 IEEE International Conference on Image Processing (ICIP) (pp. 3141\u20133145). https:\/\/doi.org\/10.1109\/ICIP46576.2022.9897365","DOI":"10.1109\/ICIP46576.2022.9897365"},{"issue":"1","key":"2441_CR19","doi-asserted-by":"publisher","first-page":"113","DOI":"10.1007\/s10845-020-01563-4","volume":"32","author":"ML Stern","year":"2021","unstructured":"Stern, M. L., & Schellenberger, M. (2021). Fully convolutional networks for chip-wise defect detection employing photoluminescence images. Journal of Intelligent Manufacturing, 32(1), 113\u2013126. https:\/\/doi.org\/10.1007\/s10845-020-01563-4","journal-title":"Journal of Intelligent Manufacturing"},{"issue":"11","key":"2441_CR20","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6501\/ac1615","volume":"32","author":"W Wang","year":"2021","unstructured":"Wang, W., Lu, X., He, Z., & Shi, T. (2021). Using convolutional neural network for intelligent SAM inspection of flip chips. Measurement Science and Technology, 32(11), 115022. https:\/\/doi.org\/10.1088\/1361-6501\/ac1615","journal-title":"Measurement Science and Technology"},{"issue":"3","key":"2441_CR21","doi-asserted-by":"publisher","first-page":"1957","DOI":"10.1007\/s00170-022-09425-4","volume":"121","author":"S Wang","year":"2022","unstructured":"Wang, S., Wang, H., Yang, F., Liu, F., & Zeng, L. (2022). Attention-based deep learning for chip-surface-defect detection. The International Journal of Advanced Manufacturing Technology, 121(3), 1957\u20131971. https:\/\/doi.org\/10.1007\/s00170-022-09425-4","journal-title":"The International Journal of Advanced Manufacturing Technology"},{"key":"2441_CR22","doi-asserted-by":"publisher","unstructured":"Wang, F., Jiang, M., Qian, C., Yang, S., Li, C., Zhang, H., Wang, X., & Tang, X. (2017). Residual attention network for image classification. In 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 6450\u20136458). https:\/\/doi.org\/10.1109\/CVPR.2017.683","DOI":"10.1109\/CVPR.2017.683"},{"key":"2441_CR23","doi-asserted-by":"publisher","unstructured":"Wang, X., Girshick, R., Gupta, A., & He, K. (2018). Non-local neural networks. In 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (pp. 7794\u20137803). https:\/\/doi.org\/10.1109\/CVPR.2018.00813","DOI":"10.1109\/CVPR.2018.00813"},{"key":"2441_CR24","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/978-3-030-01234-2_1","volume-title":"Computer vision \u2013 ECCV 2018","author":"S Woo","year":"2018","unstructured":"Woo, S., Park, J., Lee, J.-Y., & Kweon, I. S. (2018). CBAM: Convolutional block attention module. In V. Ferrari, M. Hebert, C. Sminchisescu, & Y. Weiss (Eds.), Computer vision \u2013 ECCV 2018 (pp. 3\u201319). Springer International Publishing."},{"key":"2441_CR25","doi-asserted-by":"publisher","DOI":"10.1016\/j.ress.2022.109068","volume":"232","author":"L Xia","year":"2023","unstructured":"Xia, L., Liang, Y., Leng, J., & Zheng, P. (2023). Maintenance planning recommendation of complex industrial equipment based on knowledge graph and graph neural network. Reliability Engineering & System Safety, 232, 109068. https:\/\/doi.org\/10.1016\/j.ress.2022.109068","journal-title":"Reliability Engineering & System Safety"},{"key":"2441_CR26","doi-asserted-by":"publisher","unstructured":"Xie, S., Girshick, R., Doll\u00e1r, P., Tu, Z., & He, K. (2017). Aggregated residual transformations for deep neural networks. In 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 5987\u20135995). https:\/\/doi.org\/10.1109\/CVPR.2017.634","DOI":"10.1109\/CVPR.2017.634"},{"key":"2441_CR27","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2023.106626","volume":"154","author":"Q Xu","year":"2022","unstructured":"Xu, Q., Duan, W., & He, N. (2022). DCSAU-Net: A deeper and more compact split-attention U-Net for medical image segmentation. Computers in Biology and Medicine, 154, 106626.","journal-title":"Computers in Biology and Medicine"},{"key":"2441_CR28","doi-asserted-by":"publisher","unstructured":"Xu, N., Price, B., Cohen, S., Yang, J., & Huang, T. (2016). Deep interactive object selection. In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 373\u2013381). https:\/\/doi.org\/10.1109\/CVPR.2016.47","DOI":"10.1109\/CVPR.2016.47"},{"key":"2441_CR29","doi-asserted-by":"publisher","unstructured":"Xu, N., Price, B., Cohen, S., Yang, J., & Huang, T. (2017). Deep grabcut for object selection. https:\/\/doi.org\/10.5244\/C.31.182","DOI":"10.5244\/C.31.182"},{"key":"2441_CR30","unstructured":"Yu, F., & Koltun, V. (2015). Multi-scale context aggregation by dilated convolutions. CoRR, abs\/1511.07122. https:\/\/api.semanticscholar.org\/CorpusID:17127188"},{"key":"2441_CR31","doi-asserted-by":"publisher","DOI":"10.1155\/2023\/4096164","author":"P Zheng","year":"2023","unstructured":"Zheng, P., Lou, J., Wan, X., Luo, Q., Li, Y., Xie, L., & Zhu, Z. (2023). LED chip defect detection method based on a hybrid algorithm. International Journal of Intelligent Systems. https:\/\/doi.org\/10.1155\/2023\/4096164","journal-title":"International Journal of Intelligent Systems"},{"issue":"6","key":"2441_CR32","doi-asserted-by":"publisher","first-page":"1856","DOI":"10.1109\/TMI.2019.2959609","volume":"39","author":"Z Zhou","year":"2020","unstructured":"Zhou, Z., Siddiquee, M. M. R., Tajbakhsh, N., & Liang, J. (2020). UNet++: Redesigning skip connections to exploit multiscale features in image segmentation. IEEE Transactions on Medical Imaging, 39(6), 1856\u20131867. https:\/\/doi.org\/10.1109\/TMI.2019.2959609","journal-title":"IEEE Transactions on Medical Imaging"}],"container-title":["Journal of Intelligent Manufacturing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10845-024-02441-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10845-024-02441-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10845-024-02441-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,5]],"date-time":"2025-09-05T20:16:17Z","timestamp":1757103377000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10845-024-02441-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,6,23]]},"references-count":32,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2025,8]]}},"alternative-id":["2441"],"URL":"https:\/\/doi.org\/10.1007\/s10845-024-02441-z","relation":{},"ISSN":["0956-5515","1572-8145"],"issn-type":[{"value":"0956-5515","type":"print"},{"value":"1572-8145","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,6,23]]},"assertion":[{"value":"11 December 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 May 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 June 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}