Thielen N, Pan W, Piechulek N, Voigt C, Meier S, Schmidt K, Franke J (2022)
Publication Type: Conference contribution
Publication year: 2022
Publisher: Institute of Electrical and Electronics Engineers Inc.
Pages Range: 858-863
Conference Proceedings Title: Proceedings of the 24th Electronics Packaging Technology Conference, EPTC 2022
Event location: Singapore, SGP
ISBN: 9798350398854
DOI: 10.1109/EPTC56328.2022.10013210
Transfer of solder paste for manufacturing of electronic assemblies is a crucial process step for final product quality. While the most common technologies are stencil- and screen-printing, dispensing of solder paste can be seen as an alternative, especially for three-dimensional circuit carriers and small-scale production. Nonetheless, dispensing lacks in process speed and suffers from anomalies in the process, such as insufficient solder paste transfer. In this paper, we investigated a data-driven approach to predict quality of these solder depots in terms of height, area and volume including both process data and previous dispensing quality itself. This work contains three steps. First, a suitable data set was generated by means of Design of Experiment and the dispensing steps based on it. The data was first statistically analyzed. For the model development primarily using automated machine learning (AutoML) is chosen as an approach, in order to reduce model development time and hyperparameter tuning. Furthermore, a deep neural network is trained as a comparison. The performance of the AutoML model ensemble outperforms both the neural network and the statistical benchmark for all quality parameters considered: height, area and volume of the deposited solder paste. The best models offer an R2 score of 0.71 for height, 0.89 for area and 0.81 for solder paste volume, on a separated test dataset. Thus, indicating strong correlation.
APA:
Thielen, N., Pan, W., Piechulek, N., Voigt, C., Meier, S., Schmidt, K., & Franke, J. (2022). Machine Learning Based Quality Prediction for Solder Paste Dispensing in Electronics Production. In Proceedings of the 24th Electronics Packaging Technology Conference, EPTC 2022 (pp. 858-863). Singapore, SGP: Institute of Electrical and Electronics Engineers Inc..
MLA:
Thielen, Nils, et al. "Machine Learning Based Quality Prediction for Solder Paste Dispensing in Electronics Production." Proceedings of the 24th Electronics Packaging Technology Conference, EPTC 2022, Singapore, SGP Institute of Electrical and Electronics Engineers Inc., 2022. 858-863.
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