Batch Constrained Bayesian Optimization for UltrasonicWire Bonding Feed-forward Control Design

Ultrasonic wire bonding is a solid-state joining process used to form electrical interconnections in micro andpower electronics and batteries. A high frequency oscillation causes a metallurgical bond deformation inthe contact area. Due to the numerous physical influencing factors, it is very difficult to accurately capturethis process in a model. Therefore, our goal is to determine a suitable feed-forward control strategy for thebonding process even without detailed model knowledge. We propose the use of batch constrained Bayesianoptimization for the control design. Hence, Bayesian optimization is precisely adapted to the application ofbonding: the constraint is used to check one quality feature of the process and the use of batches leads tomore efficient experiments. Our approach is suitable to determine a feed-forward control for the bondingprocess that provides very high quality bonds without using a physical model. We also show that the qualityof 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 controlparameters, 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 feedforwardcontrol without full modeling of the underlying physical processes.