Alignment of electron optical beam shaping elements using a convolutional neural network
Enzo Rotunno,Amir H. Tavabi,Paolo Rosi,Stefano Frabboni,Peter Tiemeijer,Rafal E. Dunin-Borkowski,Vincenzo Grillo +6 more
TLDR
A convolutional neural network is used to align an orbital angular momentum sorter in a transmission electron microscope and offers the possibility of real-time tuning of other electron optical devices and electron beam shaping configurations.About:
This article is published in Ultramicroscopy.The article was published on 2021-06-18 and is currently open access. It has received 13 citations till now. The article focuses on the topics: Convolutional neural network & Angular momentum.read more
Citations
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Peer ReviewDOI
Machine learning in scanning transmission electron microscopy
Sergei V. Kalinin,Colin Ophus,Paul M. Voyles,Rolf Erni,Demie Kepaptsoglou,Vincenzo Grillo,Andrew R. Lupini,Mark P. Oxley,Eric Schwenker,Maria K. Y. Chan,Joanne Etheridge,Xiang Li,Grace G. D. Han,Maxim Ziatdinov,Naoya Shibata,Stephen J. Pennycook +15 more
Journal ArticleDOI
Applications of deep learning in electron microscopy.
TL;DR: The various network architectures and error metrics that have been applied to a range of EM-related problems including denoising and inpainting are summarized.
Journal ArticleDOI
Machine learning in electron microscopy for advanced nanocharacterization: current developments, available tools and future outlook.
TL;DR: This review evaluates the state of the art of machine learning applied to electron microscopy (and obliquely, to materials and nano-sciences), and explores the state-of-the-art of other disciplines with a broader experience in applying artificial intelligence methods to their research.
Journal ArticleDOI
Near-real-time diagnosis of electron optical phase aberrations in scanning transmission electron microscopy using an artificial neural network.
Giovanni Bertoni,Enzo Rotunno,Daniel J. Marsmans,Peter Tiemeijer,Amir H. Tavabi,Rafal E. Dunin-Borkowski,Vincenzo Grillo +6 more
TL;DR: In this article , artificial intelligence can be used to provide near-real-time diagnosis of aberrations from individual Ronchigrams in a state-of-the-art scanning transmission electron microscope.
References
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Proceedings Article
Very Deep Convolutional Networks for Large-Scale Image Recognition
Karen Simonyan,Andrew Zisserman +1 more
TL;DR: In this paper, the authors investigated the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting and showed that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 layers.