Zur Seitenansicht

Titelaufnahme

Zugriffsbeschränkung
  •  Das Dokument ist frei verfügbar
Links
  • Social Media
     
     Share
  • Nachweis
    Kein Nachweis verfügbar
  • IIIF
Dateien
Klassifikation
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 novel process
    parameters from sparse data.
Statistik
  • Das PDF-Dokument wurde 16 mal heruntergeladen.