FPGA and computer-vision-based atom tracking technology for scanning probe microscopy

doi:10.1088/1674-1056/ad34cb

Abstract Atom tracking technology enhanced with innovative algorithms has been implemented in this study, utilizing a comprehensive suite of controllers and software independently developed domestically. Leveraging an on-board field-programmable gate array (FPGA) with a core frequency of 100 MHz, our system facilitates reading and writing operations across 16 channels, performing discrete incremental proportional-integral-derivative (PID) calculations within 3.4 microseconds. Building upon this foundation, gradient and extremum algorithms are further integrated, incorporating circular and spiral scanning modes with a horizontal movement accuracy of 0.38 pm. This integration enhances the real-time performance and significantly increases the accuracy of atom tracking. Atom tracking achieves an equivalent precision of at least 142 pm on a highly oriented pyrolytic graphite (HOPG) surface under room temperature atmospheric conditions. Through applying computer vision and image processing algorithms, atom tracking can be used when scanning a large area. The techniques primarily consist of two algorithms: the region of interest (ROI)-based feature matching algorithm, which achieves 97.92% accuracy, and the feature description-based matching algorithm, with an impressive 99.99% accuracy. Both implementation approaches have been tested for scanner drift measurements, and these technologies are scalable and applicable in various domains of scanning probe microscopy with broad application prospects in the field of nanoengineering.

Keywords: atom tracking FPGA computer vision drift measurement

Received: 27 February 2024
Revised: 13 March 2024
Accepted manuscript online: 18 March 2024

PACS:	07.79.Cz	(Scanning tunneling microscopes)
	07.79.-v	(Scanning probe microscopes and components)
	07.05.Pj	(Image processing)
	42.30.Tz	(Computer vision; robotic vision)

Fund: Project supported by the National Science Fund for Distinguished Young Scholars (Grant No. T2125014), the Special Fund for Research on National Major Research Instruments of the National Natural Science Foundation of China (Grant No. 11927808), and the CAS Key Technology Research and Development Team Project (Grant No. GJJSTD20200005).

Corresponding Authors: Qing Huan
E-mail: huanq@iphy.ac.cn

Cite this article:

Feng-Du Yu(俞风度), Li Liu(刘利), Su-Ke Wang(王肃珂), Xin-Biao Zhang(张新彪), Le Lei(雷乐), Yuan-Zhi Huang(黄远志), Rui-Song Ma(马瑞松), and Qing Huan(郇庆) FPGA and computer-vision-based atom tracking technology for scanning probe microscopy 2024 Chin. Phys. B 33 050705

[1] Swartzentruber B S 1996 Phys. Rev. Lett. 76 459
[2] Borovsky B, Krueger M and Ganz E 1999 Phys. Rev. B 59 1598
[3] Hill E, Freelon B and Ganz E 1999 Phys. Rev. B 60 15896
[4] Qin X, Swartzentruber B S and Lagally M 2000 Phys. Rev. Lett. 85 3660
[5] Sato T, Kitamura S I and Iwatsuki M 2000 Journal of Vacuum Science & Technology A: Vacuum, Surfaces, and Films 18 960
[6] Abe M, Sugimoto Y, Custance O and Morita S 2005 Appl. Phys. Lett. 87 173503
[7] Abe M, Sugimoto Y, Namikawa T, Morita K, Oyabu N and Morita S 2007 Appl. Phys. Lett. 90 203103
[8] Guoqiang Y, Anwei L and Xiaotang H 1999 Aviation Precision ManufacturingTechnology 35 35
[9] Pohl D W and Möller R 1988 Rev. Sci. Instrum. 59 840
[10] Dri C, Panighel M, Tiemann D, Patera L L, Troiano G, Fukamori Y, Knoller F, Lechner B A J, Cautero G, Giuressi D, Comelli G, Fraxedas J, Africh C and Esch F 2019 Ultramicroscopy 205 49
[11] Jacky J P, Garbini J L, Ettus M and Sidles J A 2008 Rev. Sci. Instrum. 79 123705
[12] Picone R A R, Davis S, Devine C, Garbini J L and Sidles J A 2017 Rev. Sci. Instrum. 88 045108
[13] Ghosal S, Pradhan S and Salapaka M 2018 Rev. Sci. Instrum. 89 056103
[14] Xie S and Ren J 2019 Mechatronics 57 86
[15] Liao H S, Akhtar I, Werner C, Slipets R, Pereda J, Wang J H, Raun E, Norgaard L O, Dons F E and Hwu E E T 2022 HardwareX 12 e00341
[16] Kocur V, Hegrova V, Patocka M, Neuman J and Herout A 2023 Ultramicroscopy 246 113666
[17] Rahman Laskar M A and Celano U 2023 APL Machine Learning 1 041501
[18] Stirling J, Woolley R A J and Moriarty P 2013 Rev. Sci. Instrum. 84 113701
[19] Sotres J, Boyd H and Gonzalez-Martinez J F 2021 Nanoscale 13 9193
[20] Liu Y, Yang J, Lawrie B J, Kelley K P, Ziatdinov M, Kalinin S V and Ahmadi M 2023 ACS Nano 17 9647
[21] Choudhary K, Garrity K F, Camp C, Kalinin S V, Vasudevan R, Ziatdinov M and Tavazza F 2021 Sci. Data 8 57
[22] Rade J, Zhang J, Sarkar S, Krishnamurthy A, Ren J and Sarkar A 2022 Bioengineering (Basel) 9 522
[23] Diao Z, Hou L and Abe M 2023 Appl. Phys. Express 16 085002
[24] Diao Z, Ueda K, Hou L, Yamashita H, Custance O and Abe M 2023 Appl. Phys. Lett. 122 121601
[25] Dickbreder T, Sabath F, Holtkemeier L, Bechstein R and Kuhnle A 2023 Beilstein J. Nanotechnol. 14 1225
[26] Teo Y R, Yong Y K and Fleming A J 2016 American Control Conference (ACC) Boston Marriott Copley Place, July 6-8, 2016, pp. 7377- 7383
[27] Gura L, Yang Z, Brinker M, Kalaß F, Kirstaedter W, Marschalik P, Junkes H, Heyde M and Freund H J 2021 Appl. Phys. Lett. 119 251601
[28] Yang Z, Gura L, Kalass F, Marschalik P, Brinker M, Kirstaedter W, Hartmann J, Thielsch G, Junkes H, Heyde M and Freund H J 2022 Rev. Sci. Instrum. 93 053704
[29] Teo Y R, Yong Y and Fleming A J 2016 Asian Journal of Control 20 1352
[30] Marinello F, Balcon M, Schiavuta P, Carmignato S and Savio E 2011 Measurement Science and Technology 22 094016
[31] Esch F, Dri C, Spessot A, Africh C, Cautero G, Giuressi D, Sergo R, Tommasini R and Comelli G 2011 Rev. Sci. Instrum. 82 053702
[32] Nartova A V, Mashukov M Y, Astakhov R R, Kudinov V Y, Matveev A V and Okunev A G 2022 Catalysts 12 135
[33] Kalinin S V, Mukherjee D, Roccapriore K, Blaiszik B J, Ghosh A, Ziatdinov M A, Al-Najjar A, Doty C, Akers S, Rao N S, Agar J C and Spurgeon S R 2023 npj Computational Materials 9 227
[34] Khan A, Lee C H, Huang P Y and Clark B K 2023 npj Computational Materials 9 85

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FPGA and computer-vision-based atom tracking technology for scanning probe microscopy

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