论文标题
使用深卷积神经网络的TSV挤出形态分类
TSV Extrusion Morphology Classification Using Deep Convolutional Neural Networks
论文作者
论文摘要
在本文中，我们利用深层卷积神经网络（CNN）在三维（3D）综合电路（ICS）中通过（TSV）挤出对通过（TSV）的挤出的形态进行分类。 TSV挤出是一个至关重要的可靠性问题，可以在3D IC中变形和破裂互连层并导致设备故障。在此，白光干涉法（WLI）技术用于获得挤出的TSV的表面轮廓。我们已经开发了一个程序，该程序使用从WLI获得的原始数据来创建TSV挤出形态数据集，其中包括带有54x54像素的TSV图像，这些图像被标记并分为三个形态学类。实施和培训了四个具有不同网络复杂性的CNN体系结构，用于TSV挤出形态分类应用程序。使用数据增强和辍学方法来实现CNN模型中过度拟合和不足之间的平衡。结果表明，具有优化复杂性，辍学和数据增强的CNN模型可以达到与人类专家相当的分类精度。
In this paper, we utilize deep convolutional neural networks (CNNs) to classify the morphology of through-silicon via (TSV) extrusion in three dimensional (3D) integrated circuits (ICs). TSV extrusion is a crucial reliability concern which can deform and crack interconnect layers in 3D ICs and cause device failures. Herein, the white light interferometry (WLI) technique is used to obtain the surface profile of the extruded TSVs. We have developed a program that uses raw data obtained from WLI to create a TSV extrusion morphology dataset, including TSV images with 54x54 pixels that are labeled and categorized into three morphology classes. Four CNN architectures with different network complexities are implemented and trained for TSV extrusion morphology classification application. Data augmentation and dropout approaches are utilized to realize a balance between overfitting and underfitting in the CNN models. Results obtained show that the CNN model with optimized complexity, dropout, and data augmentation can achieve a classification accuracy comparable to that of a human expert.
