Data-driven Modeling of the Ultrasonic Softening Effect for Robust Copper Wire Bonding
Data-driven Modeling of the Ultrasonic Softening Effect for Robust Copper Wire Bonding
复制标题
坚固铜线键合超声波软化效应的数据驱动建模
DOI：
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发表时间：
2014
期刊：
影响因子：
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通讯作者：
D. Bolowski
中科院分区：
文献类型：
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作者：
A. Unger;W. Sextro;Simon Althoff;T. Meyer;M. Brökelmann;K. Neumann;R. F. Reinhart;K. Guth;D. Bolowski
在电力电子学中，超声波引线键合用于连接电源模块的电气端子。为了实现超声引线键合机的自优化技术，建立一个过程模型是必不可少的。该模型需要考虑所谓的超声波软化效应。这是引线键合过程中的一个关键影响，主要实现了引线和衬底之间的坚固互连。然而，超声软化效应的物理建模是出了名的困难，因为它具有高度的非线性，并且缺乏合适的测量方法。首先，本文通过实验研究了超声软化对线材变形特性的影响，验证了建立超声软化模型的重要性。在第二步中，本文提出了超声软化效应的数据驱动模型，该模型是利用机器学习技术从数据中构建的。数据驱动建模的一个典型警告是需要覆盖所考虑的工艺参数领域的训练数据，以便实现训练模型对新工艺配置的准确概括。但在实际应用中，只能对工艺参数的空间进行稀疏采样。本文应用了一种新的技术，能够将关于过程的先验知识集成到数据驱动的建模过程中。结果表明，该方法能够从稀疏数据中准确地将数据驱动模型概括为不可见的过程参数。
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.