Batch Constrained Bayesian Optimization for Ultrasonic Wire Bonding Feed-forward Control Design

Michael Hesse, Michael Hesse, Matthias Hunstig, Julia Timmermann, Ansgar Trächtler, Ansgar Trächtler

2022

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 feed-forward control without full modeling of the underlying physical processes.

Paper Citation

in Harvard Style

Hesse M., Hunstig M., Timmermann J. and Trächtler A. (2022). Batch Constrained Bayesian Optimization for Ultrasonic Wire Bonding Feed-forward Control Design. In Proceedings of the 11th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-758-549-4, pages 383-394. DOI: 10.5220/0010806600003122

in Bibtex Style

@conference{icpram22,
author={Michael Hesse and Matthias Hunstig and Julia Timmermann and Ansgar Trächtler},
title={Batch Constrained Bayesian Optimization for Ultrasonic Wire Bonding Feed-forward Control Design},
booktitle={Proceedings of the 11th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2022},
pages={383-394},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010806600003122},
isbn={978-989-758-549-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 11th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - Batch Constrained Bayesian Optimization for Ultrasonic Wire Bonding Feed-forward Control Design
SN - 978-989-758-549-4
AU - Hesse M.
AU - Hunstig M.
AU - Timmermann J.
AU - Trächtler A.
PY - 2022
SP - 383
EP - 394
DO - 10.5220/0010806600003122