AI-enabled high-resolution scanning coherent diffraction imaging

被引:62
|
作者
Cherukara, Mathew J. [1 ]
Zhou, Tao [2 ]
Nashed, Youssef [3 ]
Enfedaque, Pablo [4 ]
Hexemer, Alex [4 ]
Harder, Ross J. [1 ]
Holt, Martin, V [2 ]
机构
[1] Argonne Natl Lab, Adv Photon Source, Lemont, IL 60439 USA
[2] Argonne Natl Lab, Ctr Nanoscale Mat, Lemont, IL 60439 USA
[3] Stats Perform, Chicago, IL 60601 USA
[4] Lawrence Berkeley Natl Lab, CAMERA, Berkeley, CA 94720 USA
关键词
22;
D O I
10.1063/5.0013065
中图分类号
O59 [应用物理学];
学科分类号
摘要
Ptychographic imaging is a powerful means of imaging beyond the resolution limits of typical x-ray optics. Recovering images from raw ptychographic data, however, requires the solution of an inverse problem, namely, phase retrieval. Phase retrieval algorithms are computationally expensive, which precludes real-time imaging. In this work, we propose PtychoNN, an approach to solve the ptychography data inversion problem based on a deep convolutional neural network. We demonstrate how the proposed method can be used to predict real-space structure and phase at each scan point solely from the corresponding far-field diffraction data. Our results demonstrate the practical application of machine learning to recover high fidelity amplitude and phase contrast images of a real sample hundreds of times faster than current ptychography reconstruction packages. Furthermore, by overcoming the constraints of iterative model-based methods, we can significantly relax sampling constraints on data acquisition while still producing an excellent image of the sample. Besides drastically accelerating acquisition and analysis, this capability has profound implications for the imaging of dose sensitive, dynamic, and extremely voluminous samples.
引用
收藏
页数:5
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