About this Abstract |
Meeting |
MS&T22: Materials Science & Technology
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Symposium
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AI for Big Data Problems in Advanced Imaging, Materials Modeling and Automated Synthesis
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Presentation Title |
Real-time and Large FOV Ptychography through AI@Edge |
Author(s) |
Anakha Babu, Tao Zhou, Saugat Kandel, Yi Jiang, Yudong Yao, Sinisa Veselli, Zhengchun Liu, Tekin Bicer, Martin V Holt, Antonino Miceli, Mathew J Cherukara |
On-Site Speaker (Planned) |
Tao Zhou |
Abstract Scope |
X-ray ptychography is a high-resolution imaging technique that relies on the oversampling of the real space information with scattering from a coherent x-ray beam. We present PtychoNN, a novel approach to solve the phase retrieval problem based on deep convolutional neural networks. Trained on experimentally reconstructed data, PtychoNN is both accurate and sample-beamline agnostic. It is capable of predicting phase at a speed up to 100 times faster compared to conventional iterative methods, achieving phase retrieval in real time. Moreover, PtychoNN infers on single shot diffraction patterns rather than an ensemble of data, thus removing the oversampling constraints in ptychography while enlarging its field of view in the process.We shall present results from a recent demonstration of PtychoNN at the edge, performed on the HXN beamline at the APS. We show live stitching of large field-of-view ptychography at 2.5 Hz and real time phase retrieval at 100 Hz. |