Batch Constrained Bayesian Optimization for UltrasonicWire Bonding Feed-forward Control Design
M. Hesse, M. Hunstig, J. Timmermann, A. Trächtler, in: Proceedings of the 11th International Conference on Pattern Recognition Applications and Methods (ICPRAM), 2022, pp. 383–394.
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Conference Paper
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Abstract
Ultrasonic wire bonding is a solid-state joining process used to form electrical interconnections in micro and
power electronics and batteries. A high frequency oscillation causes a metallurgical bond deformation in
the contact area. Due to the numerous physical influencing factors, it is very difficult to accurately capture
this process in a model. Therefore, our goal is to determine a suitable feed-forward control strategy for the
bonding process even without detailed model knowledge. We propose the use of batch constrained Bayesian
optimization for the control design. Hence, Bayesian optimization is precisely adapted to the application of
bonding: the constraint is used to check one quality feature of the process and the use of batches leads to
more efficient experiments. Our approach is suitable to determine a feed-forward control for the bonding
process that provides very high quality bonds without using a physical model. We also show that the quality
of the Bayesian optimization based control outperforms random search as well as manual search by a user.
Using a simple prior knowledge model derived from data further improves the quality of the connection.
The Bayesian optimization approach offers the possibility to perform a sensitivity analysis of the control
parameters, which allows to evaluate the influence of each control parameter on the bond quality. In summary,
Bayesian optimization applied to the bonding process provides an excellent opportunity to develop a feedforward
control without full modeling of the underlying physical processes.
Publishing Year
Proceedings Title
Proceedings of the 11th International Conference on Pattern Recognition Applications and Methods (ICPRAM)
Page
383-394
Conference
11th International Conference on Pattern Recognition Applications and Methods
Conference Location
Online
Conference Date
2022-02-03 – 2022-02-05
ISBN
LibreCat-ID
Cite this
Hesse M, Hunstig M, Timmermann J, Trächtler A. Batch Constrained Bayesian Optimization for UltrasonicWire Bonding Feed-forward Control Design. In: Proceedings of the 11th International Conference on Pattern Recognition Applications and Methods (ICPRAM). ; 2022:383-394.
Hesse, M., Hunstig, M., Timmermann, J., & Trächtler, A. (2022). Batch Constrained Bayesian Optimization for UltrasonicWire Bonding Feed-forward Control Design. Proceedings of the 11th International Conference on Pattern Recognition Applications and Methods (ICPRAM), 383–394.
@inproceedings{Hesse_Hunstig_Timmermann_Trächtler_2022, title={Batch Constrained Bayesian Optimization for UltrasonicWire Bonding Feed-forward Control Design}, booktitle={Proceedings of the 11th International Conference on Pattern Recognition Applications and Methods (ICPRAM)}, author={Hesse, Michael and Hunstig, Matthias and Timmermann, Julia and Trächtler, Ansgar}, year={2022}, pages={383–394} }
Hesse, Michael, Matthias Hunstig, Julia Timmermann, and Ansgar Trächtler. “Batch Constrained Bayesian Optimization for UltrasonicWire Bonding Feed-Forward Control Design.” In Proceedings of the 11th International Conference on Pattern Recognition Applications and Methods (ICPRAM), 383–94, 2022.
M. Hesse, M. Hunstig, J. Timmermann, and A. Trächtler, “Batch Constrained Bayesian Optimization for UltrasonicWire Bonding Feed-forward Control Design,” in Proceedings of the 11th International Conference on Pattern Recognition Applications and Methods (ICPRAM), Online, 2022, pp. 383–394.
Hesse, Michael, et al. “Batch Constrained Bayesian Optimization for UltrasonicWire Bonding Feed-Forward Control Design.” Proceedings of the 11th International Conference on Pattern Recognition Applications and Methods (ICPRAM), 2022, pp. 383–94.