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- W4377698189 abstract "3D X-ray imaging plays an important role in package level failure analysis. Like most other microscopies, Xray microscopes (XRM) generally have field of view (FOV) limits for high-resolution imaging. As precise fault isolation becomes more challenging in large and complex IC packages, acquiring numerous high-resolution images to search for defects in a large FOV is required. For example, if a suspect fault region is 785 mm <sup xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>3</sup> in a cylinder with 10mm in diameter and 10mm in height, more than 125 high-resolution scans with 6.28 mm <sup xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>3</sup> each are required to cover the volume. This significantly diminishes XRM imaging throughput. In this report, we propose a new deep learning reconstruction method to address the issue of achieving high-resolution at large FOVs. This AI powered technique and workflow can be used to restore the resolution from a large FOV scan." @default.
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- W4377698189 date "2023-04-19" @default.
- W4377698189 modified "2023-10-17" @default.
- W4377698189 title "A Deep Learning Reconstruction Technique and Workflow to Enhance 3D X-ray Imaging Resolution and Speed for Electronics Package Failure Analysis" @default.
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- W4377698189 doi "https://doi.org/10.23919/icep58572.2023.10129701" @default.
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