Compressive Feedback-Based Motion Control for Nanomanipulation—Theory and Applications

Conventional scanning probe microscopy (SPM)-based nanomanipulations always have to face scanner accuracy problems such as hysteresis, nonlinearity, and thermal drift. Although some scanners consist of internal position sensors, the sensitivity is not high enough to monitor high-resolution nanomanipulations. Additionally, once the scan size decreases to a nanolevel such as less than 100 nm, the noise brought by sensors is large enough to affect the performance of the closed-loop motion control system. In this paper, a non-vector space control strategy based on compressive feedback is proposed in order to improve the accuracy of SPM-based nanomanipulations. In this approach, local images (or compressive data) are used as both the reference input and feedback for a non-vector space closed-loop controller which considers the local image (or compressive data) as a set. The controller is designed in non-vector space, and it requires no prior information on features or landmarks which are widely used in traditional visual servoing. In this paper, the atomic force microscopy is used as an example of SPM to implement the non-vector space control strategy for nanomanipulations. The motivation of designing such a non-vector space controller is to solve the accuracy problem in nanomanipulation. Without this technique, the SPM-based nanomanipulations, such as nanomeasurement and nanosurgery, are difficult to conduct, with accuracy controlled under several nanometers. In order to illustrate the contributions and potential applications of this non-vector controller, at the end of this paper, an application of carbon nanotube local electrical property characterization based on a non-vector space motion control is shown to clearly verify the concept. Compared with other research in the local electrical property characterization, the non-vector space controller can ensure that the measurement accuracy (position error) is controlled within a few nanometers, which also ensures the reliability of measurement results. Additionally, this non-vector space control method can be implemented into any kind of SPM to realize a real-time control for nanomanipulation such as nanofabrication and nanoassembly.

[1]  S. Gambhir,et al.  Quantum Dots for Live Cells, in Vivo Imaging, and Diagnostics , 2005, Science.

[2]  Guangyong Li,et al.  Development of augmented reality system for AFM-based nanomanipulation , 2004, IEEE/ASME Transactions on Mechatronics.

[3]  Ning Xi,et al.  Video Rate Atomic Force Microscopy: Use of compressive scanning for nanoscale video imaging , 2013, IEEE Nanotechnology Magazine.

[4]  Éric Marchand,et al.  Mutual Information-Based Visual Servoing , 2011, IEEE Transactions on Robotics.

[5]  François Chaumette,et al.  Visual servo control. I. Basic approaches , 2006, IEEE Robotics & Automation Magazine.

[6]  Anil K. Jain,et al.  A modified Hausdorff distance for object matching , 1994, Proceedings of 12th International Conference on Pattern Recognition.

[7]  Ning Xi,et al.  Analysis of keratinocytes stiffness after desmosome disruption using Atomic Force Microscopy based nanomanipulation , 2009, 2009 9th IEEE Conference on Nanotechnology (IEEE-NANO).

[8]  Éric Marchand,et al.  Improving mutual information-based visual servoing , 2010, 2010 IEEE International Conference on Robotics and Automation.

[9]  Bo Song,et al.  Non-vector space control for nanomanipulations based on compressive feedbacks , 2012, 2012 IEEE International Conference on Robotics and Automation.

[10]  Bo Song,et al.  Stability analysis of non-vector space control via compressive feedbacks , 2012, 2012 IEEE 51st IEEE Conference on Decision and Control (CDC).

[11]  N. Xi,et al.  Plasmonic-Resonant Bowtie Antenna for Carbon Nanotube Photodetectors , 2012 .

[12]  David J. Fleet,et al.  Performance of optical flow techniques , 1994, International Journal of Computer Vision.

[13]  Tae-Wook Kim,et al.  Channel-length and gate-bias dependence of contact resistance and mobility for In2O3 nanowire field effect transistors , 2007 .

[14]  Vui Ann Shim,et al.  Evolutionary algorithms for solving multi-objective travelling salesman problem , 2011 .

[15]  Christophe Collewet,et al.  Photometric Visual Servoing , 2011, IEEE Transactions on Robotics.

[16]  Christophe Collewet,et al.  Visual servoing set free from image processing , 2008, 2008 IEEE International Conference on Robotics and Automation.

[17]  D. Donoho.  For most large underdetermined systems of linear equations the minimal 𝓁1‐norm solution is also the sparsest solution , 2006 .

[18]  Bo Song,et al.  Infrared Camera Using a Single Nano-Photodetector , 2013, IEEE Sensors Journal.

[19]  Éric Marchand,et al.  Feature tracking for visual servoing purposes , 2005, Robotics Auton. Syst..

[20]  W. Rudin.  Principles of mathematical analysis , 1964 .

[21]  Luc Doyen,et al.  Mutational equations for shapes and vision-based control , 1995, Journal of Mathematical Imaging and Vision.

[22]  Yuechao Wang,et al.  Sensor Referenced Real-Time Videolization of Atomic Force Microscopy for Nanomanipulations , 2008, IEEE/ASME Transactions on Mechatronics.

[23]  Chinmay Hegde,et al.  Sampling and Recovery of Pulse Streams , 2010, IEEE Transactions on Signal Processing.

[24]  Emmanuel J. Candès,et al.  Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information , 2004, IEEE Transactions on Information Theory.

[25]  S. Tans,et al.  Room-temperature transistor based on a single carbon nanotube , 1998, Nature.

[26]  E. Candès,et al.  Sparsity and incoherence in compressive sampling , 2006, math/0611957.

[27]  E. Yu,et al.  Direct observation of ballistic and drift carrier transport regimes in InAs nanowires , 2006 .

[28]  S. Moheimani,et al.  Simultaneous Capacitive and Electrothermal Position Sensing in a Micromachined Nanopositioner , 2011, IEEE Electron Device Letters.

[29]  Andrew J. Fleming,et al.  Integrated strain and force feedback for high-performance control of piezoelectric actuators , 2010 .

[30]  Guangyong Li,et al.  "Videolized" atomic force microscopy for interactive nanomanipulation and nanoassembly , 2005, IEEE Transactions on Nanotechnology.

[31]  S. Gonda,et al.  REAL-TIME, INTERFEROMETRICALLY MEASURING ATOMIC FORCE MICROSCOPE FOR DIRECT CALIBRATION OF STANDARDS , 1999 .

[32]  Ning Xi,et al.  Development of Infrared Detectors Using Single Carbon-Nanotube-Based Field-Effect Transistors , 2010, IEEE Transactions on Nanotechnology.

[33]  Young-Jin Choi,et al.  Novel fabrication of an SnO2 nanowire gas sensor with high sensitivity , 2008, Nanotechnology.

[34]  N Xi,et al.  Gate dependent photo-responses of carbon nanotube field effect phototransistors , 2012, Nanotechnology.

[35]  Philippe Leclère,et al.  Nanoscale investigation of the electrical properties in semiconductor polymer-carbon nanotube hybrid materials. , 2012, Nanoscale.

[36]  Rostislav V. Lapshin,et al.  Automatic drift elimination in probe microscope images based on techniques of counter-scanning and topography feature recognition , 2007 .

[37]  Seth Hutchinson,et al.  Visual Servo Control Part I: Basic Approaches , 2006 .

[38]  N. Xi,et al.  Cellular-Level Surgery Using Nano Robots , 2012, Journal of laboratory automation.

[39]  K. Kuhnen,et al.  Inverse control of systems with hysteresis and creep , 2001 .

[40]  Gregory D. Hager,et al.  Kernel-based visual servoing , 2007, 2007 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[41]  Zhiyong Fan,et al.  Electrical properties of ZnO nanowire field effect transistors characterized with scanning probes , 2005 .

[42]  E. Williams,et al.  Characterization of the electrical contact between a conductive atomic force microscope cantilever and a carbon nanotube , 2011 .