| SPECIAL TOPIC — Stephen J. Pennycook: A research life in atomic-resolution STEM and EELS |
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Real-time four-dimensional scanning transmission electron microscopy through sparse sampling |
| A W Robinson1,†, J Wells1, A Moshtaghpour3, D Nicholls1, C Huang3, A Velazco-Torrejon3, G Nicotra4, A I Kirkland3, and N D Browning1,6 |
1 SenseAI Innovations Ltd., Liverpool, UK;
2 Centre for Doctoral Training, University of Liverpool, Liverpool, UK;
3 Correlative Imaging Group, Rosalind Franklin Institute, Oxford, UK;
4 CNR-IMM, Z. I. VIII Strada 5, Catania, 95121, Italy;
5 Department of Mechanical, Materials, and Aerospace Engineering, University of Liverpool, Liverpool, UK |
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Abstract Four-dimensional scanning transmission electron microscopy (4-D STEM) is a state-of-the-art image acquisition mode used to reveal high and low mass elements at atomic resolution. The acquisition of the electron momenta at each real space probe location allows for various analyses to be performed from a single dataset, including virtual imaging, electric field analysis, as well as analytical or iterative extraction of the object induced phase shift. However, the limiting factor in 4-D STEM is the speed of acquisition which is bottlenecked by the read-out speed of the camera, which must capture a convergent beam electron diffraction (CBED) pattern at each probe position in the scan. Recent developments in sparse sampling and image inpainting (a branch of compressive sensing) for STEM have allowed for real-time recovery of sparsely acquired data from fixed monolithic detectors, Further developments in compressive sensing for 4-D STEM have also demonstrated that acquisition speeds can be increased, i.e., live video rate 4-D imaging is now possible. In this work, we demonstrate the first practical implementations of compressive 4-D STEM for real-time inference on two different scanning transmission electron microscopes.
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Received: 23 August 2024
Revised: 16 October 2024
Accepted manuscript online: 23 October 2024
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PACS:
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68.37.Ma
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(Scanning transmission electron microscopy (STEM))
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61.72.uf
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(Ge and Si)
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07.05.Pj
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(Image processing)
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07.05.Hd
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(Data acquisition: hardware and software)
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| Fund: The authors would like to thank the Rosalind Franklin Institute for providing access to the JEOL GrandARM 2 “Ruska” and National Research Council of Italy’s Institute for Microelectronics and Microsystems at Catania for providing access to the JEOL JEM-ARM 200F to gather results for this work. We thank the Royal Society for providing funding under grant number EGR10965. |
Corresponding Authors:
A W Robinson
E-mail: alex.robinson@senseai.vision
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Cite this article:
A W Robinson, J Wells, A Moshtaghpour, D Nicholls, C Huang, A Velazco-Torrejon, G Nicotra, A I Kirkland, and N D Browning Real-time four-dimensional scanning transmission electron microscopy through sparse sampling 2024 Chin. Phys. B 33 116804
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[1] Krivanek O L, Dellby N and Lupini A R 1999 Ultramicroscopy 78 1
[2] Batson P E, Dellby N and Krivanek O L 2002 Nature 418 617
[3] Faruqi A R and McMullan G 2018 Nucl. Instrum. Methods Phys. Res. A 878 180
[4] Faruqi A R, Henderson R, Pryddetch M, Allport P and Evans A 2005 Nucl. Instrum. Methods Phys. Res. A 546 170
[5] Ciston J, Johnson I J, Draney B R, Ercius P, Fong E, Goldschmidt A, Joseph J M, Lee J R, Mueller A, Ophus C, Selvarajan A, Skinner D E, Stezelberger T, Tindall C S, Minor A M and Denes P 2019 Microscopy and Microanalysis 25 1930
[6] Ryll H, Simson M, Hartmann R, Holl P, Huth M, Ihle S, Kondo Y, Kotula P, Liebel A, Müller-Caspary K, Rosenauer A, Sagawa R, Schmidt J, Soltau H and Strüder L 2016 J. Instrument. 11 P04006
[7] Yang H, Jones L, Ryll H, Simson M, Soltau H, Kondo Y, Sagawa R, Banba H, MacLaren I and Nellist P D 2015 J. Phys. Conf. Ser. 644 012032
[8] Zheng Q, Feng T, Hachtel J A, Ishikawa R, Cheng Y, Daemen L, Xing J, Idrobo J C, Yan J, Shibata N, Ikuhara Y, Sales B C, Pantelides S T and Chi M 2021 Sci. Adv. 7 eabe6819
[9] Mas-Ballesté R, Gómez-Navarro C, Gómez-Herrero J and Zamora F 2011 Nanoscale 3 20
[10] Novoselov K S, Mishchenko A, Carvalho A and Castro Neto A H 2016 Science 353
[11] Mayoral A, Mahugo R, SánchezSánchez M and Díaz I 2017 ChemCatChem 9 3497
[12] Shen B, Chen X, Shen K, Xiong H and Wei F 2020 Nat. Commun. 11 2692
[13] Pennycook S J, McGibbon A J, McGibbon M M, Browning N D, Chisholm M F and Jesson D E 1994 Determination of interface structure and bonding at atomic resolution in the STEM, in Proceedings of the 13th International Congress on Electron Microscopy, Paris (France), 17-22 July, 1994
[14] Benthem K and Pennycook S J 2009 Appl. Phys. A 96 161
[15] Chisholm M F and Pennycook S J 2006 Philosoph. Mag. 86 4699
[16] Chejarla V S, Ahmed S, Belz J, Scheunert J, Beyer A and Volz K 2023 Small Methods 7
[17] Pennycook S J, Rafferty B and Nellist P D 2000 Microscopy and Microanalysis 6 343
[18] Pennycook S J and Jesson D E 1991 Ultramicroscopy 37 14
[19] Kim Y M, Pennycook S J and Borisevich A Y 2017 Ultramicroscopy 181 1
[20] Egerton R F 2011 Electron energy-loss spectroscopy in the electron microscope (Springer Science & Business Media)
[21] Chang T Y, Tanaka Y, Ishikawa R, Toyoura K, Matsunaga K, Ikuhara Y and Shibata N 2014 Nano Lett. 14 134
[22] Ophus C 2019 Microscopy and Microanalysis 25 563
[23] Bustillo K C, Zeltmann S E, Chen M, Donohue J, Ciston J, Ophus C and Minor A M 2021 Acc. Chem. Res. 54 2543
[24] Wen Y, Ophus C, Allen C S, Fang S, Chen J, Kaxiras E, Kirkland A I and Warner J H 2019 Nano Lett. 19 6482
[25] O’Leary C M, Allen C S, Huang C, Kim J S, Liberti E, Nellist P D and Kirkland A I 2020 Appl. Phys. Lett. 116 124101
[26] Tan J A, Dull J T, Zeltmann S E, Tulyagankhodjaev J A, Johnson H M, LiebmanPeláez A, Folie B D, Dönges S A, Khatib O, Raybin J G, Roberts T D, Hamerlynck L M, Tanner C P N, Lee J, Ophus C, Bustillo K C, Raschke M B, Ohldag H, Minor A M, Rand B P and Ginsberg N S 2023 Adv. Funct. Mater. 33
[27] Nicholls D, Robinson A, Wells J, Moshtaghpour A, Bahri M, Kirkland A and Browning N 2022 Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) pp. 1586-90
[28] Nicholls D, Wells J, Robinson A W, Moshtaghpour A, Kobylynska M, Fleck R A, Kirkland A I and Browning N D 2022 arXiv: 2211.03494
[29] Robinson A W, Wells J, Nicholls D, Moshtaghpour A, Chi M, Kirkland A I and Browning N D 2023 J. Microsc. 290 53
[30] Robinson A, Nicholls D, Wells J, Moshtaghpour A, Kirkland A and Browning N D 2022 Ultramicroscopy 242 113625
[31] Robinson A W, Moshtaghpour A, Wells J, Nicholls D, Chi M, MacLaren I, Kirkland A I and Browning N D 2024 J. Microsc. 295 278
[32] Sertoglu S and Paisley J 2015 2015 23rd European Signal Processing Conference (EUSIPCO) p. 2771
[33] Zhou M, Chen H, Paisley J W, Ren L, Sapiro G and Carin L 2009 NIPS 9 2295
[34] SenseAI Innovations Ltd. Live Subsampled 4D-STEM with SenseAI |
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