Hostname: page-component-586b7cd67f-2plfb Total loading time: 0 Render date: 2024-11-27T04:40:27.886Z Has data issue: false hasContentIssue false

An Improved Visual Tracking Method in Scanning Electron Microscope

Published online by Cambridge University Press:  04 May 2012

Changhai Ru*
Affiliation:
College of Automation, Harbin Engineering University, Harbin 150001, China Robotics and Microsystems Center, Soochow University, Jiangsu 215021, China
Yong Zhang
Affiliation:
Advanced Micro and Nanosystems Laboratory, University of Toronto, Ontario M5S3G8, Canada
Haibo Huang
Affiliation:
Robotics and Microsystems Center, Soochow University, Jiangsu 215021, China
Tao Chen
Affiliation:
Robotics and Microsystems Center, Soochow University, Jiangsu 215021, China
*
Corresponding author. E-mail: [email protected]
Get access

Abstract

Since their invention, nanomanipulation systems in scanning electron microscopes (SEMs) have provided researchers with an increasing ability to interact with objects at the nanoscale. However, most nanomanipulators that are capable of generating nanometer displacement operate in an open-loop without suitable feedback mechanisms. In this article, a robust and effective tracking framework for visual servoing applications is presented inside an SEM to achieve more precise tracking manipulation and measurement. A subpixel template matching tracking algorithm based on contour models in the SEM has been developed to improve the tracking accuracy. A feed-forward controller is integrated into the control system to improve the response time. Experimental results demonstrate that a subpixel tracking accuracy is realized. Furthermore, the robustness against clutter can be achieved even in a challenging tracking environment.

Type
Techniques Development
Copyright
Copyright © Microscopy Society of America 2012

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

Davis, C.Q. & Freeman, D.M. (1998). Statistics of subpixel registration algorithms based on spatiotemporal gradients or block matching. Opt Eng 37, 12901298.CrossRefGoogle Scholar
Dietzel, D., Mönninghoff, T., Jansen, L., Fuchs, H., Ritter, C., Schwarz, U.D. & Schirmeisen, A. (2007). Interfacial friction obtained by lateral manipulation of nanoparticles using atomic force microscopy techniques. J Appl Phys 102, 084306.CrossRefGoogle Scholar
Drummond, T. & Cipolla, R. (2002). Real-time visual tracking of complex structures. IEEE Trans Pattern Anal 24, 932946.CrossRefGoogle Scholar
Eigler, D.M. & Schweizer, E.K. (1990). Positioning single atoms with a scanning tunnelling microscope. Nature 344, 524526.CrossRefGoogle Scholar
Falvo, M.R., Taylor, R.M., Helser, A., Chi, V., Brooks, F.P., Washburn, S. & Superne, R. (1999). Nanometre-scale rolling and sliding of carbon nanotubes. Nature 397, 236238.CrossRefGoogle ScholarPubMed
Fatikow, S., Eichhorn, V., Stolle, C. & Sievers, T. (2008). Development and control of a versatile nanohandling robot cell. Mechatronics 18, 370380.CrossRefGoogle Scholar
Fatikow, S., Wich, T., Hulsen, H., Sievers, T. & Jahnisch, M. (2007). Microrobot system for automatic nanohandling inside a scanning electron microscope. IEEE/ASME Trans Mechatron 12, 244252.CrossRefGoogle Scholar
Fukuda, T., Nakajima, M., Liu, P. & ElShimy, H. (2009). Nanofabrication, nanoinstrumentation and nanoassembly by nanorobotic manipulation. Int J Robotics Res 28, 537547.CrossRefGoogle Scholar
Greminger, M.A. & Nelson, B.J. (2004). Vision-based force measurement. IEEE Trans Pattern Anal 26, 290298.CrossRefGoogle ScholarPubMed
Hager, G.D. & Belhumeur, P.N. (1998). Efficient region tracking with parametric models of geometry and illumination. IEEE Trans Pattern Anal 20(10), 10251039.CrossRefGoogle Scholar
Kass, M., Witkin, A. & Terzopoulos, D. (1988). Snakes: Active contour models. Int J Comput Vision 1, 321331.CrossRefGoogle Scholar
Kratochvil, B.E., Dong, L.X. & Nelson, B.J. (2009). Real-time rigid-body visual tracking in a scanning electron microscope. Int J Robot Res 28, 498511.CrossRefGoogle Scholar
Otsu, N. (1979). A threshold selection method from gray-level histograms. IEEE Trans Syst Man Cybern 9, 962966.CrossRefGoogle Scholar
Pressigout, M. & Marchand, E. (2007). Real-time hybrid tracking using edge and texture information. Int J Robotics Res 26, 689713.CrossRefGoogle Scholar
Prior, M., Makarovski, A. & Finkelstein, G. (2007). Low-temperature conductive tip atomic force microscope for carbon nanotube probing and manipulation. Appl Phys Lett 91, 053112.CrossRefGoogle Scholar
Rosolen, G. & King, W. (1998). An automated image alignment system for the scanning electron microscope. Scanning 20, 495500.CrossRefGoogle Scholar
Ru, C. & Sun, L. (2005). Improving positioning accuracy of piezoelectric actuators by feed-forward hysteresis compensation based on a new mathematical model. Rev Sci Instrum 76, 095111.CrossRefGoogle Scholar
Rubio, F.J., Heckl, W.M. & Stark, R.W. (2005). Nanomanipulation by atomic force microscopy. Adv Eng Mater 7, 193196.CrossRefGoogle Scholar
Sierra, D.P., Weir, N.A. & Jones, J.F. (2005). A review of research in the field of nanorobotics. Technical Report SAND2005, 6808, Sandia National Laboratories.CrossRefGoogle Scholar
Sievers, T. & Fatikow, S. (2006). Real-time object tracking for the robot-based nanohandling in a scanning electron microscope. J Micromechatronics 18, 267284.CrossRefGoogle Scholar
Suga, H., Naitoh, Y., Tanaka, M., Horikawa, M., Kobori, H. & Shimizu, T. (2009). Nanomanipulation of single nanoparticle using a carbon nanotube probe in a scanning electron microscope. Appl Phys Exp 2, 055004.CrossRefGoogle Scholar
Zhi, W., Li, Q., Zhong, S. & He, S. (2005). Fast adaptive threshold for the canny edge detector. Proc. SPIE 6044, 60441.Google Scholar