Hostname: page-component-cd9895bd7-fscjk Total loading time: 0 Render date: 2024-12-28T02:49:34.158Z Has data issue: false hasContentIssue false
  • English
  • Français

Simulation and Analysis of Methods for Scanning Tunneling Microscopy Feedback Control

Published online by Cambridge University Press:  14 March 2019

Jeremy Wiedemeier ,
Greg Spencer ,
Mark J. Hagmann  and
Marwan S. Mousa
Jeremy Wiedemeier
Affiliation:
NewPath Research L.L.C., 2880 S. Main Street, Suite 214, Salt Lake City, UT, 84115, USA
Greg Spencer
Affiliation:
NewPath Research L.L.C., 2880 S. Main Street, Suite 214, Salt Lake City, UT, 84115, USA
Mark J. Hagmann*
Affiliation:
NewPath Research L.L.C., 2880 S. Main Street, Suite 214, Salt Lake City, UT, 84115, USA
Marwan S. Mousa
Affiliation:
NewPath Research L.L.C., 2880 S. Main Street, Suite 214, Salt Lake City, UT, 84115, USA
*
*Author for correspondence: Mark J. Hagmann, E-mail: newpathresearch@gmail.com
Get access

Abstract

A scanning tunneling microscope (STM) requires precise control of the tip–sample distance to maintain a constant set-point tunneling current. Typically, the tip–sample distance is controlled through the use of a control algorithm. The control algorithm takes in the measured tunneling current and returns a correction to the tip–sample distance in order to achieve and maintain the set-point value for tunneling current. We have developed an STM simulator to test the accuracy and performance of four control algorithms. The operation and effectiveness of these control algorithms are evaluated.

Keywords

Controlfeedbackmicroscopescanningtunneling
Type
Related Techniques
Information
Microscopy and Microanalysis , Volume 25 , Special Issue 2: Atom Probe Tomography and Microscopy APT&M 2018 , April 2019 , pp. 554 - 560
Copyright
Copyright © Microscopy Society of America 2019 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Anguiano, E, Oliva, AI & Aguilar, M (1998). Optimal conditions for imaging in scanning tunneling microscopy: Theory. Rev Sci Instrum 69(11), 3867–3874.Google Scholar
Binning, G, Rohrer, H, Gerber, G & Weibel, E (1982). Tunneling through a controllable vacuum gap. Appl Phys Lett 40, 178–180.Google Scholar
Hagmann, MJ, Spencer, G & Wiedemeier, J (2018). Virtual scanning tunneling microscope offered as a free-download. Micros Today 26(3), 18–23.Google Scholar
Hassan, F, Zolotas, AC & Smith, T (2017). Optimized Ziegler–Nichols based PID control design for tilt suspensions. J Eng Sci Technol Rev 10(5), 17–24.Google Scholar
Jeon, D & Willis, RF (1991). Feedback system response in a scanning tunneling microscope. Rev Sci Instrum 62(6), 1650–1651.Google Scholar
Oliva, AI, Anguiano, E, Denisenko, N, Aguilar, M & Peña, JL (1995). Analysis of scanning tunneling microscopy feedback system. Rev Sci Instrum 66(5), 3196–3203.Google Scholar
Stirling, J (2014). Control theory for scanning probe microscopy revisited. Beilstein J Nanotechnol 5, 337–345.Google Scholar
Stirling, J, Lekkas, I, Sweetman, A, Djuranovic, P, Guo, Q, Pauw, B, Granwehr, J, Lévy, R & Moriarty, P (2014). Critical assessment of the evidence for striped nanoparticles. PLoS ONE 9(11), 1–17.Google Scholar
Ziegler, JG & Nichols, NB (1942). Optimum settings for automatic controllers. Trans ASME 64, 759–768.Google Scholar