TY  - BOOK
AB  - 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 novel process
parameters from sparse data.
DA  - 2014
LA  - eng
PY  - 2014
SN  - 978-3-8007-3578-5
T2  - 8th International Conference on Integrated Power Electronics Systems, Proceedings, February, 25-27, 2014 Nuremberg/Germany
TI  - Modeling the Ultrasonic Softening Effect for Robust Copper Wire Bonding
UR  - https://nbn-resolving.org/urn:nbn:de:0070-pub-29007105
Y2  - 2024-11-23T11:03:29
ER  -