by Andreas Unger, Walter Sextro, Simon Althoff, Tobias Meyer, Michael Brökelmann, Klaus Neumann, René Felix Reinhart, Karsten Guth and Daniel Bolowski
Abstract:
In power electronics, ultrasonic wire bonding is used to connect the electrical terminals of power modules. To implement a self-optimization technique for ultrasonic wire bonding machines, a model of the process is essential. This model needs to include the so called ultrasonic softening effect. It is a key effect within the wire bonding process primarily enabling the robust interconnection between the wire and a substrate. However, the physical modeling of the ultrasonic softening effect is notoriously difficult because of its highly non-linear character and the absence of a proper measurement method. In a first step, this paper validates the importance of modeling the ultrasonic softening by showing its impact on the wire deformation characteristic experimentally. In a second step, the paper presents a data-driven model of the ultrasonic softening effect which is constructed from data using machine learning techniques. A typical caveat of data-driven modeling is the need for training data that cover the considered domain of process parameters in order to achieve accurate generalization of the trained model to new process configurations. In practice, however, the space of process parameters can only be sampled sparsely. In this paper, a novel technique is applied which enables the integration of prior knowledge about the process into the datadriven modeling process. It turns out that this approach results in accurate generalization of the data-driven model to unseen process parameters from sparse data.
Reference:
Unger, A.; Sextro, W.; Althoff, S.; Meyer, T.; Brökelmann, M.; Neumann, K.; Reinhart, R. F.; Guth, K.; Bolowski, D.: Data-driven Modeling of the Ultrasonic Softening Effect for Robust Copper Wire Bonding. Proceedings of 8th International Conference on Integrated Power Electronic Systems, volume 141, 2014.
Bibtex Entry:
@INPROCEEDINGS{Unger2014,
howpublished = {Conference Proceedings},
author = {Andreas Unger AND Walter Sextro AND Simon Althoff AND Tobias Meyer
AND Michael Brökelmann AND Klaus Neumann AND Ren{\'e} Felix Reinhart
AND Karsten Guth AND Daniel Bolowski},
title = {Data-driven Modeling of the Ultrasonic Softening Effect for Robust
Copper Wire Bonding},
booktitle = {Proceedings of 8th International Conference on Integrated Power Electronic
Systems},
year = {2014},
volume = {141},
pages = {175-180},
abstract = {In power electronics, ultrasonic wire bonding is used to connect the
electrical terminals of power modules. To implement a self-optimization
technique for ultrasonic wire bonding machines, a model of the process
is essential. This model needs to include the so called ultrasonic
softening effect. It is a key effect within the wire bonding process
primarily enabling the robust interconnection between the wire and
a substrate. However, the physical modeling of the ultrasonic softening
effect is notoriously difficult because of its highly non-linear
character and the absence of a proper measurement method. In a first
step, this paper validates the importance of modeling the ultrasonic
softening by showing its impact on the wire deformation characteristic
experimentally. In a second step, the paper presents a data-driven
model of the ultrasonic softening effect which is constructed from
data using machine learning techniques. A typical caveat of data-driven
modeling is the need for training data that cover the considered
domain of process parameters in order to achieve accurate generalization
of the trained model to new process configurations. In practice,
however, the space of process parameters can only be sampled sparsely.
In this paper, a novel technique is applied which enables the integration
of prior knowledge about the process into the datadriven modeling
process. It turns out that this approach results in accurate generalization
of the data-driven model to unseen process parameters from sparse
data.},
bdsk-url-1 = {http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6776789},
url = {http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6776789}
}