{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,5]],"date-time":"2025-12-05T19:18:34Z","timestamp":1764962314895,"version":"3.46.0"},"reference-count":32,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2025,12,3]],"date-time":"2025-12-03T00:00:00Z","timestamp":1764720000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"The Ministry of Science and Technology Council","award":["NSTC 114-2221-E-390-003","NSTC 114-2622-E-390-002"],"award-info":[{"award-number":["NSTC 114-2221-E-390-003","NSTC 114-2622-E-390-002"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The die bonding process significantly impacts the yield and quality of IC packaging, and its quality detection is also a critical image sensing technology. With the advancement of machine automation and increased operating speeds, the misclassification rate in die bond image inspection has also risen. Therefore, this study develops a high-speed intelligent vision inspection model that slightly improves classification accuracy and adapts to the operation of new-generation machines. Furthermore, by identifying the causes of die bonding defects, key process parameters can be adjusted in real time during production, thereby improving the yield of the die bonding process and substantially reducing manufacturing cost losses. Previously, we proposed a lightweight model named DSG\u03b2SI-YOLOv7-tiny, which integrates depthwise separable convolution, Ghost convolution, and a Sigmoid activation function with a learnable \u03b2 parameter. This model enables real-time and efficient detection and prediction of die bond quality through image sensing. We further enhanced the previous model by incorporating an SE layer, ECA-Net, Coordinate Attention, and a Small Object Enhancer to accommodate the faster operation of new machines. This improvement resulted in a more lightweight architecture named DSG\u03b2SI-SECS-YOLOv7-tiny. Compared with the previous model, the proposed model achieves an increased inference speed of 294.1 FPS and a Precision of 99.1%.<\/jats:p>","DOI":"10.3390\/s25237358","type":"journal-article","created":{"date-parts":[[2025,12,5]],"date-time":"2025-12-05T18:42:02Z","timestamp":1764960122000},"page":"7358","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["High-Speed Die Bond Quality Detection Using Lightweight Architecture DSG\u03b2SI-SECS-Yolov7-Tiny"],"prefix":"10.3390","volume":"25","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0952-3591","authenticated-orcid":false,"given":"Bao Rong","family":"Chang","sequence":"first","affiliation":[{"name":"Department of Computer Science and Information Engineering, National University of Kaohsiung, Kaohsiung 81148, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7444-753X","authenticated-orcid":false,"given":"Hsiu-Fen","family":"Tsai","sequence":"additional","affiliation":[{"name":"Department of Fragrance and Cosmetic Science, Kaohsiung Medical University, Kaohsiung 80708, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wei-Shun","family":"Chang","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Information Engineering, National University of Kaohsiung, Kaohsiung 81148, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,12,3]]},"reference":[{"key":"ref_1","unstructured":"Seon, K. 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