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Exit Wavefunction Reconstruction from Single Transmission Electron Micrographs with Deep Learning [pre-print]
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Ede, Jeffrey M., Peters, Jonathan J. P., Sloan, Jeremy and Beanland, Richard (2020) Exit Wavefunction Reconstruction from Single Transmission Electron Micrographs with Deep Learning [pre-print]. Working Paper. arXiv. (Unpublished)
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Official URL: https://arxiv.org/abs/2001.10938
Abstract
Half of wavefunction information is undetected by conventional transmission electron microscopy (CTEM) as only the intensity, and not the phase, of an image is recorded. Following successful applications of deep learning to optical hologram phase recovery, we have developed neural networks to recover phases from CTEM intensities for new datasets containing 98340 exit wavefunctions. Wavefunctions were simulated with clTEM multislice propagation for 12789 materials from the Crystallography Open Database. Our networks can recover 224x224 wavefunctions in ~25 ms for a large range of physical hyperparameters and materials, and we demonstrate that performance improves as the distribution of wavefunctions is restricted. Phase recovery with deep learning overcomes the limitations of traditional methods: it is live, not susceptible to distortions, does not require microscope modification or multiple images, and can be applied to any imaging regime. This paper introduces multiple approaches to CTEM phase recovery with deep learning, and is intended to establish starting points to be improved upon by future research. Source code and links to our new datasets and pre-trained models are available at this https URL [https://github.com/Jeffrey-Ede/one-shot].
| Item Type: | Working or Discussion Paper (Working Paper) | ||||
|---|---|---|---|---|---|
| Divisions: | Faculty of Science, Engineering and Medicine > Science > Physics | ||||
| Journal or Publication Title: | arXiv | ||||
| Publisher: | arXiv | ||||
| Official Date: | 2020 | ||||
| Dates: |
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| Number: | 2001.10938 | ||||
| Institution: | University of Warwick | ||||
| Status: | Not Peer Reviewed | ||||
| Publication Status: | Unpublished | ||||
| Access rights to Published version: | Open Access (Creative Commons) | ||||
| Description: | Half of wavefunction information is undetected by conventional transmission electron microscopy. This preprint shows that lost phase information can be recovered with deep learning for new datasets of simulated wavefunctions. |
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