  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.
