Modeling forces between the probe of atomic microscope and the scanning surface

  • Mohammad Javad Sharifi
  • Ahmad Reza Khoogar
  • Mehdi Tajdari
Original Article
  • 8 Downloads

Abstract

Atomic force microscope (AFM) is usually used to study the properties and surface structure of nanoscale materials. AFMs have three major abilities: force measurement, imaging, and manipulation. In the force measurement, AFM can be used to measure the forces between the probe and the sample as a function of their mutual separation. AFM compared to scanning electron microscope has a single image scan size; also the scanning speed of AFM is also a limitation. AFM images can also be affected by nonlinearity, hysteresis, creep of the piezoelectric material, and cross talk between the x, y, and z axes that may require software enhancement and filtering. Due to the nature of AFM probes, they cannot normally measure steep walls or overhangs in surface. In this study, the force between the Probe of Atomic Microscope and the surface is simulated by using force measurement ability of AFM and artificial neural network. The experimental data are used for training of artificial neural networks. The best model was found to be a feed-forward backpropagation network, with Logsig, Tansig and Tansig transfer functions in successive layers, respectively, and 3 and 2 neurons in the first and second hidden layers. According to the results, the proposed neural network is well capable of modeling the behavior of AFM probes in noncontact mode.

Keywords

Artificial neural network AFM MEMS Optimal design 

Notes

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

References

  1. 1.
    Burton EF, Hillier J, Prebus A (1939) Report on the development of the electron supermicroscope at Toronto. Phys Rev 56:1171–1172CrossRefGoogle Scholar
  2. 2.
    Yang Q (2007) Advanced controller design using neural networks for nonlinear dynamic systems with application to micro/nano robotics. University of Missouri–Rolla, MO, USAGoogle Scholar
  3. 3.
    Lapshin RV (1995) Analytical model for the approximation of hysteresis loop and its application to the scanning tunneling microscope. Rev Sci Instrum 66(9):4718–4730.  https://doi.org/10.1063/1.1145314 CrossRefGoogle Scholar
  4. 4.
    Lapshin RV (2004) Feature-oriented scanning methodology for probe microscopy and nanotechnology. Nanotechnology 15(9):1135–1151CrossRefGoogle Scholar
  5. 5.
    Feynman RP, Leighton R, Sands M (1964) The Feynman lectures on physics, vol II. Addison Wesley, Boston, pp 8–10Google Scholar
  6. 6.
    Leite FL, Bueno CC et al (2012) Theoretical models for surface forces and adhesion and their measurement using atomic force microscopy. Int J Mol Sci 13(10):12773–12856.  https://doi.org/10.3390/ijms131012773 CrossRefGoogle Scholar
  7. 7.
    Schalkoff RJ (1997) Artificial neural networks. McGraw-Hill, New YorkMATHGoogle Scholar
  8. 8.
    Arifuzzaman MD, Saiful Islam M, Imtiaz Hossain M (2017) Moisture damage evaluation in SBS and lime modified asphalt using AFM and artificial intelligence. Neural Comput Appl 28:125–134CrossRefGoogle Scholar
  9. 9.
    Norouzi A, Haedi M, Adineh VR (2011) Strength modeling and optimizing ultrasonic welded parts of ABS-PMMA using artificial intelligence methods. Int J Adv Manuf Technol 61:135–147.  https://doi.org/10.1007/s00170-011-3699-2 CrossRefGoogle Scholar
  10. 10.
    BraunsmannC Tilman E (2014) Artificial neural networks for the automated analysis of force map data in atomic force microscopy. Rev Sci Instrum 85(5):056104CrossRefGoogle Scholar
  11. 11.
    DME SPM (2018) Atomic force microscope scanner (AFM) DS 95-50/DS 95-200 datasheet. Semilab Germany GmbH. http://www.dme-spm.com/ds95.html. Accessed 3 Jan 2018
  12. 12.
    Cowan GR, Douglass J, Holtzman A (1964) Explosive bonding. US patent office. US 3137937 AGoogle Scholar
  13. 13.
    Skoog DA, Leary JJ (1992) Principle of instrumental analysis. Saunders College Pub, PhiladelphiaGoogle Scholar
  14. 14.
    Bird J (2010) Electrical and Electronic principles and Technology. Newnes Publishers, Oxford, pp 63–76Google Scholar

Copyright information

© The Natural Computing Applications Forum 2018

Authors and Affiliations

  1. 1.Mechanical Engineering DepartmentMalek Ashtar University of TechnologyTehranIran
  2. 2.School of Mechanical EngineeringAzad University Science and Research Branch of ArakArākIran

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