Dr. Steve Kelly Profile

Dr. Steve Kelly

at Carl Zeiss X-ray Microscopy Inc

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Proceedings Article | 12 November 2024 Presentation + Paper

Deep-learning models enable high-resolution reconstruction of large-volume x-ray microscopy datasets

Nathan Johnson, Yulia Trenikhina, Hrishikesh Bale, Steve Kelly

Proceedings Volume 13152, 131520U (2024) https://doi.org/10.1117/12.3027501

KEYWORDS: Deep learning, Data modeling, X-ray microscopy, Scanning electron microscopy, Batteries, X-rays, Tomography, Image resolution, Process modeling, Pixel resolution

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A critical challenge in modern x-ray imaging is achieving high-resolution for large volumes. Countless material applications demand statistically relevant samplings of defects, encompassing secondary phases, inclusions, phase segregation, cracks, pores, and more. There is an inherent tradeoff between the volume that can be imaged and the resolution of the image. Imaging a large volume may yield a statistically relevant sample size but at low resolution, rendering it inadequate for detecting the smallest defects. Conversely, high spatial resolution imaging captures intricate details over a small volume, raising questions about statistical relevance. This paper demonstrates the application of a commercial deep-learning-based x-ray tomography reconstruction method for solid state battery analysis. By harnessing the power of two distinct datasets—a high-volume scan and a high-resolution scan —a deep neural network is trained on correspondences between low and high-resolution images. Following training, the model performs reconstruction of the large-volume dataset at high-resolution. The model is demonstrated for defect detection in a commercial solid state battery. A 3mm² battery sample is imaged at a 3-micron pixel resolution. A second interior tomography is captured at 1.5-micron pixel resolution. The DL model reconstructs the battery volume at 1.5-micron resolution, revealing defects and particle inclusions previously concealed by low-resolution, large-volume scans. Reconstruction is validated using direct comparisons between the deep-learning-based XCT reconstruction and traditional high-resolution imaging. A femtosecond laser was employed to cut the sample, exposing these defects for imaging with scanning electron microscopy (SEM). Multiple defects and microstructural features revealed in the deep-learning-based reconstructions were confirmed during this validation process.

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