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.

中文翻译：

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使用深度卷积神经网络的 TSV 挤压形态分类

在本文中，我们利用深度卷积神经网络 (CNN) 对三维 (3D) 集成电路 (IC) 中的硅通孔 (TSV) 挤出的形态进行分类。TSV 挤出是一个关键的可靠性问题，它会使 3D IC 中的互连层变形和破裂，并导致设备故障。在此，白光干涉测量 (WLI) 技术用于获得挤出 TSV 的表面轮廓。我们开发了一个程序，该程序使用从 WLI 获得的原始数据来创建 TSV 挤压形态数据集，包括具有 54x54 像素的 TSV 图像，这些图像被标记并分为三个形态类别。为 TSV 挤出形态分类应用实现和训练了四种具有不同网络复杂度的 CNN 架构。利用数据增强和丢弃方法来实现 CNN 模型中过拟合和欠拟合之间的平衡。获得的结果表明，具有优化复杂性、dropout 和数据增强的 CNN 模型可以实现与人类专家相媲美的分类精度。