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Deep learning-assisted focused ion beam nanofabrication

Deep learning-assisted focused ion beam nanofabrication
Deep learning-assisted focused ion beam nanofabrication

Focused ion beam (FIB) milling is an important rapid prototyping tool for micro- and nanofabrication and device and materials characterization. It allows for the manufacturing of arbitrary structures in a wide variety of materials, but establishing the process parameters for a given task is a multidimensional optimization challenge, usually addressed through time-consuming, iterative trial-and-error. Here, we show that deep learning from prior experience of manufacturing can predict the postfabrication appearance of structures manufactured by focused ion beam (FIB) milling with >96% accuracy over a range of ion beam parameters, taking account of instrument- and target-specific artifacts. With predictions taking only a few milliseconds, the methodology may be deployed in near real time to expedite optimization and improve reproducibility in FIB processing.

deep learning, focused ion beam milling, nanofabrication
1530-6984
2734-2739
Buchnev, Oleksandr
60cdb0d2-3388-47be-a066-61b3b396f69d
Grant-Jacob, James
c5d144d8-3c43-4195-8e80-edd96bfda91b
Eason, R.W.
e38684c3-d18c-41b9-a4aa-def67283b020
Zheludev, Nikolai
32fb6af7-97e4-4d11-bca6-805745e40cc6
Mills, Benjamin
05f1886e-96ef-420f-b856-4115f4ab36d0
MacDonald, Kevin F.
76c84116-aad1-4973-b917-7ca63935dba5
Buchnev, Oleksandr
60cdb0d2-3388-47be-a066-61b3b396f69d
Grant-Jacob, James
c5d144d8-3c43-4195-8e80-edd96bfda91b
Eason, R.W.
e38684c3-d18c-41b9-a4aa-def67283b020
Zheludev, Nikolai
32fb6af7-97e4-4d11-bca6-805745e40cc6
Mills, Benjamin
05f1886e-96ef-420f-b856-4115f4ab36d0
MacDonald, Kevin F.
76c84116-aad1-4973-b917-7ca63935dba5

Buchnev, Oleksandr, Grant-Jacob, James, Eason, R.W., Zheludev, Nikolai, Mills, Benjamin and MacDonald, Kevin F. (2022) Deep learning-assisted focused ion beam nanofabrication. Nano Letters, 22 (7), 2734-2739. (doi:10.1021/acs.nanolett.1c04604).

Record type: Article

Abstract

Focused ion beam (FIB) milling is an important rapid prototyping tool for micro- and nanofabrication and device and materials characterization. It allows for the manufacturing of arbitrary structures in a wide variety of materials, but establishing the process parameters for a given task is a multidimensional optimization challenge, usually addressed through time-consuming, iterative trial-and-error. Here, we show that deep learning from prior experience of manufacturing can predict the postfabrication appearance of structures manufactured by focused ion beam (FIB) milling with >96% accuracy over a range of ion beam parameters, taking account of instrument- and target-specific artifacts. With predictions taking only a few milliseconds, the methodology may be deployed in near real time to expedite optimization and improve reproducibility in FIB processing.

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More information

Accepted/In Press date: 23 February 2022
Published date: 13 April 2022
Additional Information: This work was supported by the Engineering and Physical Sciences Research Council, UK [grant numbers EP/N03368 X/1, EP/T026197/1, EP/M009122/1, EP/T02643 X/1, and EP/N00762 X/1], and the Singapore Ministry of Education [MOE2016-T3-1-006]. We acknowledge the NVIDIA Academic Hardware Grant Program for donation of the Nvidia Titan Xp GPU.
Keywords: deep learning, focused ion beam milling, nanofabrication

Identifiers

Local EPrints ID: 455923
URI: http://eprints.soton.ac.uk/id/eprint/455923
ISSN: 1530-6984
PURE UUID: d5991b99-019f-4cf7-9394-7fc18cb3cfac
ORCID for James Grant-Jacob: ORCID iD orcid.org/0000-0002-4270-4247
ORCID for R.W. Eason: ORCID iD orcid.org/0000-0001-9704-2204
ORCID for Nikolai Zheludev: ORCID iD orcid.org/0000-0002-1013-6636
ORCID for Benjamin Mills: ORCID iD orcid.org/0000-0002-1784-1012
ORCID for Kevin F. MacDonald: ORCID iD orcid.org/0000-0002-3877-2976

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Date deposited: 08 Apr 2022 16:34
Last modified: 07 Oct 2022 04:01

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Contributors

Author: Oleksandr Buchnev
Author: R.W. Eason ORCID iD
Author: Benjamin Mills ORCID iD

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