Anomaly Detection for Dispensing of Solder Paste on 3D Circuit Carriers Using Machine Learning

Thielen N, Wagner M, Meier S, Voigt C, Franke J (2023)

Publication Type: Conference contribution

Publication year: 2023

Publisher: Institute of Electrical and Electronics Engineers Inc.

Pages Range: 932-937

Conference Proceedings Title: Proceedings of the 25th Electronics Packaging Technology Conference, EPTC 2023

Event location: Singapore

ISBN: 9798350329575

DOI: 10.1109/EPTC59621.2023.10457873

Abstract

This paper addresses the challenges in manufacturing of 3D mechatronic integrated devices, particularly focusing on the anomalies that occur during the transfer of solder paste as an interconnection material. Utilizing machine learning techniques, the study extends prior research done on 2D circuit carriers to identify these anomalies in 3D substrates and predicts solder paste depot quality in general. The research employs a design of experiments to develop a comprehensive dataset, taking into account the unique properties of 3D substrates. The paper also presents a low-cost camera and segmentation algorithm setup to measure quality parameters like the area and offset of solder paste, as conventional systems fall short in this context. The system has a very good measurement capability for determining the area of the solder paste. Based on the data obtained, transfer learning techniques are utilized to leverage existing 2D data for model training, showing a significant improvement in model performance and especially reducing training times. Specifically, an R2 score of 0.88 is achieved using transfer learning, compared to 0.83 with only 3D data, while an AutoML-model even reaches an R2 score of 0.94. The study suggests that AI-based methods offer cost-effective alternatives to established systems, enabling flexible manufacturing with fewer iterations and lower setup costs.

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How to cite

APA:

Thielen, N., Wagner, M., Meier, S., Voigt, C., & Franke, J. (2023). Anomaly Detection for Dispensing of Solder Paste on 3D Circuit Carriers Using Machine Learning. In Andrew Tay, King Jien Chui, Yeow Kheng Lim, Chuan Seng Tan, Sunmi Shin (Eds.), Proceedings of the 25th Electronics Packaging Technology Conference, EPTC 2023 (pp. 932-937). Singapore, SG: Institute of Electrical and Electronics Engineers Inc..

MLA:

Thielen, Nils, et al. "Anomaly Detection for Dispensing of Solder Paste on 3D Circuit Carriers Using Machine Learning." Proceedings of the 25th Electronics Packaging Technology Conference, EPTC 2023, Singapore Ed. Andrew Tay, King Jien Chui, Yeow Kheng Lim, Chuan Seng Tan, Sunmi Shin, Institute of Electrical and Electronics Engineers Inc., 2023. 932-937.

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