{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,18]],"date-time":"2026-06-18T16:10:41Z","timestamp":1781799041957,"version":"3.54.5"},"reference-count":49,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2023,6,9]],"date-time":"2023-06-09T00:00:00Z","timestamp":1686268800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Career Development Fund","award":["C210812046"],"award-info":[{"award-number":["C210812046"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Recent advancements in 3D deep learning have led to significant progress in improving accuracy and reducing processing time, with applications spanning various domains such as medical imaging, robotics, and autonomous vehicle navigation for identifying and segmenting different structures. In this study, we employ the latest developments in 3D semi-supervised learning to create cutting-edge models for the 3D object detection and segmentation of buried structures in high-resolution X-ray semiconductors scans. We illustrate our approach to locating the region of interest of the structures, their individual components, and their void defects. We showcase how semi-supervised learning is utilized to capitalize on the vast amounts of available unlabeled data to enhance both detection and segmentation performance. Additionally, we explore the benefit of contrastive learning in the data pre-selection step for our detection model and multi-scale Mean Teacher training paradigm in 3D semantic segmentation to achieve better performance compared with the state of the art. Our extensive experiments have shown that our method achieves competitive performance and is able to outperform by up to 16% on object detection and 7.8% on semantic segmentation. Additionally, our automated metrology package shows a mean error of less than 2 \u03bcm for key features such as Bond Line Thickness and pad misalignment.<\/jats:p>","DOI":"10.3390\/s23125470","type":"journal-article","created":{"date-parts":[[2023,6,9]],"date-time":"2023-06-09T09:54:32Z","timestamp":1686304472000},"page":"5470","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Robust Detection, Segmentation, and Metrology of High Bandwidth Memory 3D Scans Using an Improved Semi-Supervised Deep Learning Approach"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-3083-6103","authenticated-orcid":false,"given":"Jie","family":"Wang","sequence":"first","affiliation":[{"name":"Institute for Infocomm Research (I2R), Agency for Science, Technology and Research (A*STAR), 1 Fusionopolis Way, #21-01, Connexis South Tower, Singapore 138632, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1748-8498","authenticated-orcid":false,"given":"Richard","family":"Chang","sequence":"additional","affiliation":[{"name":"Institute for Infocomm Research (I2R), Agency for Science, Technology and Research (A*STAR), 1 Fusionopolis Way, #21-01, Connexis South Tower, Singapore 138632, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4403-825X","authenticated-orcid":false,"given":"Ziyuan","family":"Zhao","sequence":"additional","affiliation":[{"name":"Institute for Infocomm Research (I2R), Agency for Science, Technology and Research (A*STAR), 1 Fusionopolis Way, #21-01, Connexis South Tower, Singapore 138632, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5613-0515","authenticated-orcid":false,"given":"Ramanpreet Singh","family":"Pahwa","sequence":"additional","affiliation":[{"name":"Institute for Infocomm Research (I2R), Agency for Science, Technology and Research (A*STAR), 1 Fusionopolis Way, #21-01, Connexis South Tower, Singapore 138632, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,6,9]]},"reference":[{"key":"ref_1","unstructured":"Pahwa, R.S., Lay Nwe, M.T., Chang, R., Min, O.Z., Jie, W., Gopalakrishnan, S., Soon Wee, D.H., Qin, R., Rao, V.S., and Dai, H. (July, January 1). Automated Attribute Measurements of Buried Package Features in 3D X-ray Images using Deep Learning. Proceedings of the IEEE 71st Electronic Components and Technology Conference (ECTC), Virtual."},{"key":"ref_2","unstructured":"Jie, W., Chang, R., Xun, X., Lile, C., Foo, C.S., and Pahwa, R.S. (June, January 31). Improved Bump Detection and Defect Identification for HBMs using Refined Machine Learning Approach. Proceedings of the IEEE 24th Electronics Packaging Technology Conference (EPTC), San Diego, CA, USA."},{"key":"ref_3","unstructured":"Balasubramaniam, A., and Pasricha, S. (2022). Object Detection in Autonomous Vehicles: Status and Open Challenges. arXiv."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Rahman, H., Bukht, T.F.N., Imran, A., Tariq, J., Tu, S., and Alzahrani, A. (2022). A Deep Learning Approach for Liver and Tumor Segmentation in CT Images Using ResUNet. Bioengineering, 9.","DOI":"10.3390\/bioengineering9080368"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Chang, R., Pahwa, R.S., Wang, J., Chen, L., Satini, S., Wan, K.W., and Hsu, D. (2022, January 11\u201313). Creating Semi-supervised learning-based Adaptable Object Detection Models for Autonomous Service Robot. Proceedings of the 12th Conference on Learning Factories (CLF), Singapore.","DOI":"10.2139\/ssrn.4075994"},{"key":"ref_6","unstructured":"Redmon, J., and Farhadi, A. (2018). YOLOv3: An Incremental Improvement. arXiv."},{"key":"ref_7","unstructured":"Wang, C.Y., Bochkovskiy, A., and Liao, H.Y.M. (2022). YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors. arXiv."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S.E., Fu, C., and Berg, A.C. (2015). SSD: Single Shot MultiBox Detector. arXiv.","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"ref_9","first-page":"91","article-title":"Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks","volume":"Volume 28","author":"Ren","year":"2015","journal-title":"Proceedings of the Advances in Neural Information Processing Systems"},{"key":"ref_10","unstructured":"Yang, A. (2019). 3D Object Detection from CT Scans Using a Slice-and-Fuse Approach. [Ph.D. Thesis, Robotics Institute CMU]."},{"key":"ref_11","unstructured":"Pahwa, R.S., Chang, R., Jie, W., Xun, X., Zaw Min, O., Sheng, F.C., Ser Choong, C., and Rao, V.S. (June, January 31). Automated Detection and Segmentation of HBMs in 3D X-ray Images using Semi-Supervised Deep Learning. Proceedings of the IEEE 72nd Electronic Components and Technology Conference (ECTC), San Diego, CA, USA."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Lin, T., Maire, M., Belongie, S., Bourdev, L.D., Girshick, R.B., Hays, J., Perona, P., Ramanan, D., Doll\u00e1r, P., and Zitnick, C.L. (2014). Microsoft COCO: Common Objects in Context. arXiv.","DOI":"10.1007\/978-3-319-10602-1_48"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Gao, J., Wang, J., Dai, S., Li, L., and Nevatia, R. (2018). NOTE-RCNN: NOise Tolerant Ensemble RCNN for Semi-Supervised Object Detection. arXiv.","DOI":"10.1109\/ICCV.2019.00960"},{"key":"ref_14","unstructured":"Hoffman, J., Guadarrama, S., Tzeng, E., Donahue, J., Girshick, R.B., Darrell, T., and Saenko, K. (2014). LSDA: Large Scale Detection Through Adaptation. arXiv."},{"key":"ref_15","unstructured":"Sohn, K., Zhang, Z., Li, C., Zhang, H., Lee, C., and Pfister, T. (2020). A Simple Semi-Supervised Learning Framework for Object Detection. arXiv."},{"key":"ref_16","unstructured":"Jeong, J., Lee, S., Kim, J., and Kwak, N. (2019, January 8\u201314). Consistency-based Semi-supervised Learning for Object detection. Proceedings of the Advances in Neural Information Processing Systems, Montreal, QC, Canada."},{"key":"ref_17","unstructured":"Tarvainen, A., and Valpola, H. (2017). Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results. arXiv."},{"key":"ref_18","unstructured":"Liu, Y., Ma, C., He, Z., Kuo, C., Chen, K., Zhang, P., Wu, B., Kira, Z., and Vajda, P. (2021). Unbiased Teacher for Semi-Supervised Object Detection. arXiv."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"He, K., Fan, H., Wu, Y., Xie, S., and Girshick, R. (2020). Momentum Contrast for Unsupervised Visual Representation Learning. arXiv.","DOI":"10.1109\/CVPR42600.2020.00975"},{"key":"ref_20","unstructured":"Chen, T., Kornblith, S., Norouzi, M., and Hinton, G. (2020). A Simple Framework for Contrastive Learning of Visual Representations. arXiv."},{"key":"ref_21","unstructured":"Hadsell, R., Chopra, S., and LeCun, Y. (2006, January 17\u201322). Dimensionality Reduction by Learning an Invariant Mapping. Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR\u201906), New York, NY, USA."},{"key":"ref_22","unstructured":"Ghahramani, Z., Welling, M., Cortes, C., Lawrence, N., and Weinberger, K. (2014, January 8\u201314). Generative Adversarial Nets. Proceedings of the Advances in Neural Information Processing Systems, Montreal, QC, Canada."},{"key":"ref_23","unstructured":"Donahue, J., and Simonyan, K. (2019). Large Scale Adversarial Representation Learning. arXiv."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Bai, W., Oktay, O., Sinclair, M., Suzuki, H., Rajchl, M., Tarroni, G., Glocker, B., King, A., Matthews, P.M., and Rueckert, D. (2017, January 11\u201313). Semi-supervised Learning for Network-Based Cardiac MR Image Segmentation. Proceedings of the Medical Image Computing and Computer-Assisted Intervention (MICCAI), Montreal, QC, Canada.","DOI":"10.1007\/978-3-319-66185-8_29"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Long, J., Shelhamer, E., and Darrell, T. (2015). Fully Convolutional Networks for Semantic Segmentation. arXiv.","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., and Brox, T. (2015). U-Net: Convolutional Networks for Biomedical Image Segmentation. arXiv.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Milletari, F., Navab, N., and Ahmadi, S. (2016, January 25\u201328). V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation. Proceedings of the International Conference on 3D Vision (3DV), Stanford, CA, USA.","DOI":"10.1109\/3DV.2016.79"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1065","DOI":"10.1109\/TMI.2020.3046692","article-title":"Analyzing Overfitting Under Class Imbalance in Neural Networks for Image Segmentation","volume":"40","author":"Li","year":"2021","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_29","unstructured":"French, G., Laine, S., Aila, T., Mackiewicz, M., and Finlayson, G. (2019). Semi-supervised semantic segmentation needs strong, varied perturbations. arXiv."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Goyal, P., Girshick, R., He, K., and Doll\u00e1r, P. (2017). Focal Loss for Dense Object Detection. arXiv.","DOI":"10.1109\/ICCV.2017.324"},{"key":"ref_31","unstructured":"Liu, W., Wen, Y., Yu, Z., and Yang, M. (2016). Large-Margin Softmax Loss for Convolutional Neural Networks. arXiv."},{"key":"ref_32","unstructured":"Hung, W.C., Tsai, Y.H., Liou, Y.T., Lin, Y.Y., and Yang, M.H. (2018, January 3\u20136). Adversarial learning for semi-supervised semantic segmentation. Proceedings of the British Machine Vision Conference, London, UK."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Yang, L., Zhuo, W., Qi, L., Shi, Y., and Gao, Y. (2022). ST++: Make Self-training Work Better for Semi-supervised Semantic Segmentation. arXiv.","DOI":"10.1109\/CVPR52688.2022.00423"},{"key":"ref_34","unstructured":"Li, S., Zhang, C., and He, X. (2020). Medical Image Computing and Computer Assisted Intervention\u2014MICCAI 2020, Springer International Publishing."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Yu, L., Wang, S., Li, X., Fu, C.W., and Heng, P.A. (2019, January 13\u201317). Uncertainty-aware self-ensembling model for semi-supervised 3D left atrium segmentation. Proceedings of the Medical Image Computing and Computer Assisted Intervention\u2013MICCAI 2019: 22nd International Conference, Shenzhen, China. Proceedings, Part II 22.","DOI":"10.1007\/978-3-030-32245-8_67"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"523","DOI":"10.1109\/TNNLS.2020.2995319","article-title":"Transformation-consistent self-ensembling model for semisupervised medical image segmentation","volume":"32","author":"Li","year":"2020","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Luo, X., Chen, J., Song, T., and Wang, G. (2020). Semi-supervised Medical Image Segmentation through Dual-task Consistency. arXiv.","DOI":"10.1609\/aaai.v35i10.17066"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Li, S., Zhao, Z., Xu, K., Zeng, Z., and Guan, C. (2021, January 1\u20135). Hierarchical consistency regularized mean teacher for semi-supervised 3d left atrium segmentation. Proceedings of the 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), Virtual Conference.","DOI":"10.1109\/EMBC46164.2021.9629941"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Zhao, Z., Xu, K., Yeo, H.Z., Yang, X., and Guan, C. (2023). MS-MT: Multi-Scale Mean Teacher with Contrastive Unpaired Translation for Cross-Modality Vestibular Schwannoma and Cochlea Segmentation. arXiv.","DOI":"10.1007\/978-3-031-44153-0_7"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"3744","DOI":"10.1109\/JBHI.2021.3052320","article-title":"Dsal: Deeply supervised active learning from strong and weak labelers for biomedical image segmentation","volume":"25","author":"Zhao","year":"2021","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_41","unstructured":"Wu, Y., Kirillov, A., Massa, F., Lo, W.Y., and Girshick, R. (2023, May 22). Detectron2. Available online: https:\/\/github.com\/facebookresearch\/detectron2."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Lin, T., Doll\u00e1r, P., Girshick, R.B., He, K., Hariharan, B., and Belongie, S. (2016). Feature Pyramid Networks for Object Detection. arXiv.","DOI":"10.1109\/CVPR.2017.106"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2015). Deep Residual Learning for Image Recognition. arXiv.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1007\/s11263-014-0733-5","article-title":"The Pascal Visual Object Classes Challenge: A Retrospective","volume":"111","author":"Everingham","year":"2015","journal-title":"Int. J. Comput. Vis."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Pahwa, R.S., Ho, S.W., Qin, R., Chang, R., Min, O.Z., Jie, W., Rao, V.S., Nwe, T.L., Yang, Y., and Neumann, J.T. (2020, January 13\u201330). Machine-Learning Based Methodologies for 3D X-Ray Measurement, Characterization and Optimization for Buried Structures in Advanced IC Packages. Proceedings of the International Wafer Level Packaging Conference (IWLPC), Virtual.","DOI":"10.23919\/IWLPC52010.2020.9375903"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Pahwa, R.S., Nwe, T.L., Chang, R., Jie, W., Min, O.Z., Ho, S.W., Qin, R., Rao, V.S., Yang, Y., and Neumann, J.T. (2020, January 2\u20134). Deep Learning Analysis of 3D X-ray Images for Automated Object Detection and Attribute Measurement of Buried Package Features. Proceedings of the IEEE 22nd Electronics Packaging Technology Conference (EPTC), Singapore.","DOI":"10.1109\/EPTC50525.2020.9315043"},{"key":"ref_47","unstructured":"Lewis, D.D., and Gale, W.A. (2023, May 22). A Sequential Algorithm for Training Text Classifiers. Available online: https:\/\/link.springer.com\/chapter\/10.1007\/978-1-4471-2099-5_1."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Scheffer, T., Decomain, C., and Wrobel, S. (2001, January 13\u201315). Active Hidden Markov Models for Information Extraction. Proceedings of the Advances in Intelligent Data Analysis, Cascais, Portugal.","DOI":"10.1007\/3-540-44816-0_31"},{"key":"ref_49","unstructured":"Settles, B. (2009). Active Learning Literature Survey, Computer Sciences Technical Report 1648; University of Wisconsin\u2013Madison."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/12\/5470\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T19:51:53Z","timestamp":1760125913000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/12\/5470"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,6,9]]},"references-count":49,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2023,6]]}},"alternative-id":["s23125470"],"URL":"https:\/\/doi.org\/10.3390\/s23125470","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,6,9]]}}}