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Deep Learning is being widely used to identify and segment various structures in 2D and 3D scans in fields such as robotics and medical imaging. We leverage this exciting technology to train state-of-the-art models for 3D object detection and segmentation for various buried structures such as Through Silicon Vias (TSVs), memory bumps, and logic bumps. We show in detail how we fabricate our wafers and generate 3D scans. Thereafter, we explain our approach in locating these different structures in 3D scans and how we further segment these structures into solders, voids, Cu-Pillars, and Cu-Pads for 3D metrology and defect identification. We compare our approach with state-of-the-art techniques and perform a thorough analysis to discuss the advantages and disadvantages of various approaches in each step.

For over 40 years lithographic silicon scaling has driven circuit integration and performance improvement in the semiconductor industry. As silicon scaling slows down, the industry is increasingly dependent on IC package technologies to contribute to further circuit integration and performance improvements. This is a paradigm shift and requires the IC package industry to reduce the size and increase the density of internal interconnects on a scale which has never been done before. Traditional package characterization and process optimization relies on destructive techniques such as physical cross-sections and delayering to extract data from internal package features. These destructive techniques are not practical with today's advanced packages. In this paper we will demonstrate how data acquired non-destructively with a 3D X-ray microscope can be enhanced and optimized using machine learning, and can then be used to measure, characterize and optimize the design and production of buried interconnects in advanced IC packages. Test vehicles replicating 2.5D and HBM construction were designed and fabricated, and digital data was extracted from these test vehicles using 3D X-ray and machine learning techniques. The extracted digital data was used to characterize and optimize the design and production of the interconnects and demonstrates a superior alternative to destructive physical analysis. We report an mAP of 0.96 for 3D object detection, a dice score of 0.92 for 3D segmentation, and an average of 2.1um error for 3D metrology on the test dataset. This paper is the first part of a multi-part report., Comment: 7 pages, 9 figures

Failure analysis is crucial in improving semiconductor manufacturing yields. Yield improvement is done by collecting, analyzing, identifying the causes of defects, and applying corrective actions to resolve the root causes. With the ongoing miniaturization of TSVs, micro-bumps, RDLs, and other package interconnects [1], detecting defects in these buried interconnects is becoming more difficult as well as more important. Traditionally semiconductor packages are cross-sectioned to identify internal process defects such as unsolders, solder shorts, and pad misalignment. Cross-sectioning is a destructive approach, is difficult to do, and provides information in a single 2D plane only. Due to the large effort and the destructive nature of this approach, the amount of data that can be generated is typically quite limited. The development of 3D x-ray microscopy provides industry with the capability to image and analyze buried features such as micro-bumps, TSVs, and other metallic structures using a non-destructive, 3-dimensional technology [2]. At the same time, deep learning has revolutionized other technologies such as visual surveillance, predictive maintenance [3], object detection [4], and is now revolutionizing defect detection in semiconductors. When used together, the combination of 3D x-ray microscopy and deep learning is establishing a new paradigm in package inspection and metrology. In this paper, we will present a novel method for automatically detecting internal anomalies in semiconductor packages and using deep learning to assess the attributes of these interconnects. Chips representative of stacked 2.5D packages were fabricated and assembled using thermo-compression bonding. Bonding parameters were varied in order to create packages with different bond line thickness, different solder fillet shapes, and various pad alignment scenarios. A commercial 3D x-ray imaging tool was used to create high-quality tomographies of these packages. Deep-learning and computer vision-based methods were employed to automatically detect internal features and measure attributes. A three-step procedure was used for data analysis. In the first step, a bounding box was detected for each region of interest (Copper Pillar, Pad, etc.) using a modified single shot detector object model. In the second step, we isolated features within the region of interest and performed 3D segmentation on them. The third and final step utilized automated 3D metrology using the segmented regions. Robust 3D computer vision techniques were deployed to measure the extent of voids which are key attributes for the chip fabrication and process control step. This is the first part of a multi-part paper.

This paper demonstrates the use of 3D Anisotropic Convolutional Neural Network (CNN) with predict-refine mechanism for 3D brain tumor segmentation. We propose two networks that utilize multi-scale feedback and saliency maps respectively to segment three critical regions involved in automated brain tumor segmentation. The proposed networks are formulated to predict feature maps at different resolutions during the prediction phase. These networks perform refinement process using the saliency or feature maps as feedback information for the refinement process. The recurrent architecture allows the network to automatically rectify errors in saliency map of the previous prediction phase resulting in more reliable final predictions. Our experimental results on the BraTS2017 dataset demonstrate the superior performance of our proposed predict-refine architecture than current state of the art approaches improving results by up to 8% without any additional increase in the 1.9M model parameters.

The core binding factor (CBF) gene RUNX1 is a target of chromosomal translocations in leukemia, including t(8;21) in acute myeloid leukemia (AML). Normal CBF function is essential for activity of AML1-ETO, product of the t(8;21), and for survival of several leukemias lacking RUNX1 mutations. Using virtual screening and optimization, we developed Runt domain inhibitors which bind to the Runt domain and disrupt its interaction with CBFβ. On-target activity was demonstrated by the Runt domain inhibitors' ability to depress hematopoietic cell formation in zebrafish embryos, reduce growth and induce apoptosis of t(8;21) AML cell lines, and reduce progenitor activity of mouse and human leukemia cells harboring the t(8;21), but not normal bone marrow cells. Runt domain inhibitors had similar effects on murine and human T cell acute lymphocytic leukemia (T-ALL) cell lines. Our results confirmed that Runt domain inhibitors might prove efficacious in various AMLs and in T-ALL.