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1-5 of 5
Masako Terada
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Proceedings Papers
ISTFA2023, ISTFA 2023: Conference Proceedings from the 49th International Symposium for Testing and Failure Analysis, 432-435, November 12–16, 2023,
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In this work we present a new defect localization capability on Wafer Level Chip Scale Packages (WLCSP) with small-scale Cu pillars using advanced 3D X-ray microscopy (XRM). In comparison to conventional microcomputed tomography (Micro-CT or microCT) flat-panel technology, the synchrotron-based optically enhanced 3D X-ray microscopy can detect very small defects with submicron resolutions. Two case studies on actual failures (one from the assembly process and one from reliability testing) will be discussed to demonstrate this powerful defect localization technique. Using the tool has helped speed up the failure analysis (FA) process by locating the defects non-destructively in a matter of hours instead of days or weeks as needed with destructive physical failure analysis.
Proceedings Papers
ISTFA2023, ISTFA 2023: Conference Proceedings from the 49th International Symposium for Testing and Failure Analysis, 443-447, November 12–16, 2023,
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3D X-ray microscopy (XRM) is an effective highresolution and non-destructive tool for semiconductor package level failure analysis. One limitation with XRM is the ability to achieve high-resolution 3D images over large fields of view (FOVs) within acceptable scan times. As modern semiconductor packages become more complex, there are increasing demands for 3D X-ray instruments to image encapsulated structures and failures with high productivity and efficiency. With the challenge to precisely localize fault regions, it may require high-resolution imaging with a FOV of tens of millimeters. This may take over hundreds of hours of scans if many high-resolution but small-volume scans are performed and followed with the conventional 3D registration and stitches. In this work, a novel deep learning reconstruction method and workflow to address the issue of achieving highresolution imaging over a large FOV is reported. The AI powered technique and workflow can be used to restore the resolution over the large FOV scan with only a high-resolution and a large FOV scan. Additionally, the 3D registration and stitch workflow are automated to achieve the large FOV images with a recovered resolution comparable to the actual high-resolution scan.
Proceedings Papers
ISTFA2023, ISTFA 2023: Conference Proceedings from the 49th International Symposium for Testing and Failure Analysis, 554-560, November 12–16, 2023,
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This paper presents advanced workflows that combine 3D Xray microscopy (XRM), nanoscale tomography, and electron microscopy to generate a detailed visualization of the interior of electronic devices and assemblies to enable the study of internal components for failure analysis (FA). Newly developed techniques such as the integration of deep-learning (DL) based algorithms for 3D image reconstruction are also discussed in this article. In addition, a DL-based tool (called DeepScout) is introduced that uses high-resolution 3D XRM datasets as training data for lower-resolution, larger field-of-view datasets and scales larger-volume data using a neural network model. Ultimately, these workflows can be run independently or complementary to other multiscale correlative microscopy evaluations, e.g., electron microscopy, and will provide valuable insights into the inner workings of electronic packages and integrated circuits at multiple length scales, from macroscopic features on electronic devices (i.e., hundreds of mm) to microscopic details in electronic components (in the tens of nm). Understanding advanced electronic systems through X-ray imaging and electron microscopy, and possibly complemented with some additional correlative microscopy investigations, can speed development time, increase cost efficiency, and simplify FA and quality inspection of printed circuit boards (PCBs) and electronic devices assembled with new emerging technologies.
Proceedings Papers
ISTFA2022, ISTFA 2022: Conference Proceedings from the 48th International Symposium for Testing and Failure Analysis, 319-323, October 30–November 3, 2022,
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Microscopic imaging and characterization of semiconductor devices and material properties often begin with a sample preparation step. A variety of sample preparation methods such as mechanical lapping and broad ion beam (BIB) milling have been widely used in physical failure analysis (FPA) workflows, allowing internal defects to be analyzed with high-resolution scanning electron microscopy (SEM). However, these traditional methods become less effective for more complicated semiconductor devices, because the cross-sectioning accuracy and reliability do not satisfy the need to inspect nanometer scale structures. Recent trends on multi-chip stacking and heterogenous integration exacerbate the ineffectiveness. Additionally, the surface prepared by these methods are not sufficient for high-resolution imaging, often resulting in distorted sample information. In this work, we report a novel correlative workflow to improve the cross-sectioning accuracy and generate distortion-free surface for SEM analysis. Several semiconductor samples were imaged with 3D X-ray microscopy (XRM) in a non-destructive manner, yielding volumetric data for users to visualize and navigate at submicron accuracy in three dimensions. With the XRM data to serve as 3D maps of true package structures, the possibility to miss or destroy the fault regions is largely eliminated in PFA workflows. In addition to the correlative workflow, we will also demonstrate a proprietary micromachining process which is capable of preparing deformation-free surfaces for SEM analysis.
Proceedings Papers
ISTFA2021, ISTFA 2021: Conference Proceedings from the 47th International Symposium for Testing and Failure Analysis, 291-295, October 31–November 4, 2021,
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3D X-ray tomography plays a critical role in electronic device failure analysis, but it can take several hours to overnight to get sufficient resolution in fault regions to detect and identify defects. In this paper, we propose a machine learning based reconstruction technique that can speed up data acquisition by a factor of four or more, while maintaining image quality. The method, which uses neural networks, extracts signals from low-dose data more efficiently than the conventional Feldkamp-Davis-Kress (FDK) approach, which is sensitive to noise and prone to aliasing errors. Several semiconductor packages and a commercial smartwatch battery module are analyzed using the new technique and the results compared with those obtained using conventional methods. The neural network can be trained on as little as one tomography image and the only requirement for the training data is that the sample or region of interest is well represented with all characteristic features in the field-of-view.