Electron optics for a multi-pass transmission electron microscope

Mankos, M.; Koppell, Stewart A.; Klopfer, Brannon B.; Juffmann, Thomas; Kolařík, Vladimír; Shadman, K.; Kasevich, Mark A.

doi:10.1016/bs.aiep.2019.08.003

“…For example, by using aberration correctors 136,[157][158][159] , choosing scan shapes and speeds that minimize distortions 138 , and using stable sample holders to reduce drift 160 . Beam damage can also be reduced by using minimal electron voltage and electron dose [161][162][163] , or dose-fractionation across multiple frames in multi-pass TEM [164][165][166] or STEM 167 .…”

Section: Improving Signal-to-noisementioning

confidence: 99%

Review: Deep Learning in Electron Microscopy

Ede

2020

Preprint

0

View full text Add to dashboard Cite

Deep learning is transforming most areas of science and technology, including electron microscopy. This review paper offers a practical perspective aimed at developers with limited familiarity. For context, we review popular applications of deep learning in electron microscopy. Following, we discuss hardware and software needed to get started with deep learning and interface with electron microscopes. We then review neural network components, popular architectures, and their optimization. Finally, we discuss future directions of deep learning in electron microscopy.

show abstract

A simulation program for electron mirrors using Boundary Element Method

Munro¹,

Liu²,

Rouse³

et al. 2019

Advances in Imaging and Electron Physics Including Proceedings CPO-10

0

View full text Add to dashboard Cite

Deep learning in electron microscopy

Ede

¹

2021

Mach. Learn.: Sci. Technol.

View full text Add to dashboard Cite

Deep learning is transforming most areas of science and technology, including electron microscopy. This review paper offers a practical perspective aimed at developers with limited familiarity. For context, we review popular applications of deep learning in electron microscopy. Following, we discuss hardware and software needed to get started with deep learning and interface with electron microscopes. We then review neural network components, popular architectures, and their optimization. Finally, we discuss future directions of deep learning in electron microscopy.

show abstract

Electron optics for a multi-pass transmission electron microscope

Cited by 3 publications

References 11 publications

Review: Deep Learning in Electron Microscopy

Review: Deep Learning in Electron Microscopy

A simulation program for electron mirrors using Boundary Element Method

Deep learning in electron microscopy

Contact Info

Product

Resources

About