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Conventional inspection methods for Through-Silicon Via (TSV) 3D shape defect detection, such as Scanning Electron Microscopy (SEM) and X-ray inspection, have been widely used due to their precision in structural analysis. However, these methods suffer from significant limitations including high equipment cost, long inspection time, and the inability to operate in real-time. Moreover, SEM is inherently a destructive technique, while X-ray imaging lacks sufficient resolution to detect nanoscale shape anomalies or polymer residues. These drawbacks hinder the implementation of fast and scalable inspection systems, which are increasingly demanded in modern semiconductor manufacturing, especially for high-density 3D integration. Therefore, a new approach is urgently required—one that ensures high detection accuracy while also being non-destructive, fast, and suitable for realtime inspection in practical production environments. In this paper, we develop a real-time TSV 3D shape defect inspection system implemented with a deep learning-based object detection method. For the real-time operation of the proposed system, YOLOv8 and YOLOv10 are utilized because the YOLO family of networks can guarantee fast inference performance as well as excellent detection performance. The YOLOv8 and YOLOv10 have intrinsic differences in the network architecture, such as anchor-free detection structure and NMS-free training and a dualhead structure, resulting in different inference and detection performance. Therefore, based on the performance comparison of the two networks, the appropriate model should be selected according to the specific needs for either faster inference or higher detection accuracy. In addition, for more reliable training of object detection networks, we collect 3D point cloud data containing TSV normal and defective pattern data created on real 8-inch silicon wafers.

By obtaining the datasets from real silicon wafers, we can ensure the reliability and the practical applicability of the trained network performance. For the performance comparison, we utilize several performance metrics, which are processing time, precision, F1 score, and Fβ score. Finally, extensive evaluations confirm that the YOLOv8-l model achieves the highest precision (0.99997) and F1 score (0.99989), while the YOLOv10-n model exhibited the fastest processing time (0.18601 seconds) and the highest Fβ score (0.92565).

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기사명 저자명 페이지 원문 목차
Precision matching optimization of optical metrology and inspection equipment for yield enhancement in semiconductor manufacturing Hyoseop Shin, Hojun Lee, Dongkun Shin p. 623-632
(An) error-aware 4-2 compressor design for balanced accuracy and efficiency in approximate multipliers Dongju Kim, Yongtae Kim p. 633-644
Real-time TSV 3D shape defect inspection system using deep learning based fast object detection Kyeong Beom Park, Jae Yeol Lee, Harim Lee p. 645-653
Coplanar waveguide sensor for dimethyl methyl phosphonate (DMMP) vapor detection at microwave frequencies Zabdiel Brito-Brito, Jorge A.I. Araujo, Sung-min Sim, Marcos T. de Melo, Ignacio Llamas-Garro, Jung-Mu Kim p. 654-661
Hardware-software co-design for vector similarity search on HBM-PIM Nahyeon Kim, Sujin Kim, Min Jung, Haechannuri Noh, Ji-Hoon Kim p. 662-669
(A) 64-channel high-compliance neural stimulator IC in standard CMOS with sub-1nC charge balancing for seizure suppression Seokbeom Cheon, Seungah Lee, Byeongseol Kim, Joonsung Bae p. 670-678
(An) area-efficient two-step vernier time-to-digital converter with a metastability-free phase detector for NAND flash memory interfaces Dong-Ho Shin, Jun-Ha Lee, Kang Yoon Lee p. 679-687
Energy efficient CMOS stochastic bit-based Bayesian inference accelerator Honggu Kim, Yong Shim p. 688-695
Enhanced methane gas sensors utilizing lithium-ion decorated SWCNTs networks Da-Gyo Yoo, Kyung Eun Kim, Ryang Ha Kim, Beom Joon Jung, Jae Hyeon Kim, Young Lae Kim, Myung-Hyun Baek p. 696-702
Room temperature hydrogen gas sensor based on Pd-SnO2 nanomaterials with electro-spinning Dongjun Jang, Sangwan Kim, Min-Woo Kwon p. 703-710
(A) 1.12-ps resolution flash ADC-assisted coarse-to-fine time-to-digital converter with adaptive reference-voltage calibration and digital linearity correction Solmon Shin, Hyunwoo Son, Youngsik Kim, Shinwoong Kim p. 711-720
Deep learning driven modeling of advanced node FinFET Sehtab Hossain p. 721-729
(An) FVF-based capacitorless LDO with segmented power cells achieving fast transient response, wideband high PSR, and wide load current range Doojin Jang p. 730-735

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