1 / 38

R ESEARCH D IRECTIONS

R ESEARCH D IRECTIONS. Srinivasa M. Salapaka Laboratory for Information and Decision Systems Massachusetts Institute of Technology. Department of Mechanical Engineering Iowa State University March 25, 2003. Outline. Research Directions Nanopositioning Micro-Cantilever Dynamics

les
Download Presentation

R ESEARCH D IRECTIONS

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. RESEARCH DIRECTIONS Srinivasa M. Salapaka Laboratory for Information and Decision Systems Massachusetts Institute of Technology Department of Mechanical Engineering Iowa State University March 25, 2003

  2. Outline • Research Directions • Nanopositioning • Micro-Cantilever Dynamics • Nanofriction • Clustering Algorithms • Image deblurring

  3. ROBUST BROADBAND NANOPOSITIONING

  4. MOTIVATION • Nanopositioning • High Bandwidth • High throughputs • High throughput requirements in probing material surfaces • Binding affinity between materials, other properties • High speed requirements for studying biosystems • Cell dynamics, probing living systems • Faster scanning requirements in various engineering applications • Ultra high density data reading and writing • Enabling feature in many studies and applications • Studies of cell dynamics require micro/nano-second imaging capabilities • Ultrahigh precision • Specifications are often in the angstrom regime • In scanning probe technologies molecular and atomic forces are routinely probed • Robustness • Necessary for reliability in view of • Uncertainty in model and environment • Diverse users –do not have the engineering expertise

  5. MOTIVATION • Nanopositioning system • High precision (probing at nanoscale) • High bandwidth (high throughputs) • Robustness (reliability and repeatability) Needs of Combinatorial Chemistry

  6. OBJECTIVE • Robust Broadband Nanopositioning System with • 500 Hz for large scans (100 m £ 100 m) • nanometer resolution • 1 MHz for small scans (2 m £ 2 m) • subnanometer resolution • Compatible for scanning probe applications

  7. APPROACH • Novel Device Architecture • Novel paradigm for robustness, bandwidth and resolution

  8. Proposed design • Two stage scanning • Large Scans • Motion possible by flexure based design • Sample-holders on steel platforms • Heavy (smaller bandwidths) • Actuation by stack-piezos • Large forces, large travels (100 m) • Small Scans • Cylindrical Piezoactuators • Sample kept on actuator itself • Smaller travels (2 m) • Lighter (higher bandwidths) • Integrate the two • Put the small scanner on top of large scanner

  9. Head top EOD, Laser Laser to photodiode Head Laser from EOD Mirror Microcantilever Microcantilever Holder Support Plate X-Y-Z small range nanopositioner Large range nanopositioner A Schematic of PROPOSED Nanoscope

  10. Large Range Scanner

  11. PRESENT STATUS AND FUTURE DIRECTIONS • Developed a precise paradigm to address: • High Bandwidth • High Resolution • Robustness • Modern control tools • Model the plant • Quantify and characterize the challenges • Design feedback laws • Practically eliminated hysteresis and creep • Obtained 60-70 times improvement in the bandwidth over current popular systems • Substantial improvement in the reliability and repeatability

  12. Results (cont’d.) creep hysteresis bandwidth Repeatability Reliability tracking

  13. Results • Large Scanners • Identified and addressed design challenges • on bandwidth, precision and robustness • Piezo actuation is predominant; hysteresis and creep nonlinearities, design constraints • Sensors can deteriorate open loop performance • Employed modern control tools to address these challenges and achieved • Performance • controllers to achieve the desired tradeoff between resolution and bandwidth • Robustness • By addressing model uncertainties

  14. Preview based control design • Improve tracking performance • For a priori known reference trajectories Feed forward Controller Plant + - • feedforward controller in addition to feedback controller • To give desired input ud such that Gud(t)=xr(t) Anticipatory Control design for better tracking performance

  15. Preliminary Simulation Results • significant improvement in performance • Substantial reduction in error

  16. Multi-Input Multi-Output Control Design Gxx Gxy ¼ 0 Gyx Gyy

  17. Multi-Input Multi-Output Control Design • MIMO design • Significant coupling effects • Gyx greater than Gyy in some frequencies • Carry out control design for the MIMO model • Glover McFarlane, Nominal and Robust H1 • Multi-objective design • Actuation constraints • Specified by H1 norm • Resolution specifications • addressed by H2 norm Control Design for plant model that includes X-Y coupling

  18. Integration into the nanoscope • Integrate the probing head with the positioning system • Sample holder capable of moving in Z direction • Control of tip-sample separation • MIMO control design • for positioner and cantilever system (3 £ 3 model) • Account for tip-sample interactions • Nonlinear models • Observer based control design • z-displacements are measured but velocities are not measured • Observers useful for compensation designs for nanofriction Control Design for plant model that include positioning (X-Y) and probing (Z) aspects

  19. Short Range Scanner • For high bandwidth • Low mass essential • Cylindrical piezos – scanner cum actuator • Can be run open-loop • Inverse dynamic schemes • Inverse hysteresis models • Alternatively use closed loop control loop design • Design/implement sensors for detection of lateral motion • Employ the control design procedure as done for large range scanner Smaller lighter scanner implies faster scanning

  20. Lateral motion sensors for AFM • Previous experience • Designed sensors for shell piezos (J scanner in an AFM) • Designed sensors based on optical levers • Used them for feedback • Loop shaping control laws • Obtained substantial improvement in the performance • Resolution in order of few nm (1kHz) • Bandwidth improvement of over 20 times

  21. Another untried approach • Build a new nanoscope with control design in mind • Make small scanners • Lighter and therefore high resonant frequencies • Faster scanners • Bigger coupling effects • More burden on control design • Upshot • Simpler device design • More emphasis on control design • Achieve higher bandwidths Shift the emphasis from device design to control design and achieve faster scanning rates

  22. CLUSTERING

  23. X What Is Clustering? • Clustering • Separation of set of objects into groups such that objects in one group are more ‘similar’ than those in other • find the optimal partition {Rj} of the domain  and the allocation of representative locations • Combinatorially complex problem • Interpret and design d(x,rj) • Adapt and modify Deterministic Annealing Algorithm • Simulations

  24. MOTIVATION • Chemoinformatics, Combinatorial Discovery • Search by elimination through a ‘chemical space’ for a ‘backbone’ compound (drug discovery) • Enormous number of possible molecular combinations • Requires clustering algorithms to narrow the search • Essential in data mining, data compression, facility location, machine learning

  25. OBJECTIVE Develop and adapt clustering algorithms for Combinatorial Discovery

  26. Present status and future directions • Partition a ‘large’ space ‘optimally’ into a given number of ‘cells’ and specify ‘representative locations under constraints • Similar to dividing ‘chemical space’ into clusters with representative elements • Developed fast algorithms under which • a new class of problems were for the first time identified • precise mathematical formulations were provided • Algorithms developed that are fast • The developed algorithms utilized on real life systems

  27. EXAMPLE SYSTEM

  28. IMAGE RECONSTRUCTION

  29. MOTIVATION • Blurred images in scanning probe microscopy • The tip-geometry convolves with the sample to provide a blurred image

  30. Objective • Deblurred using deconvolution methods • Modeled as convolution equation: y=h*x • y is observed data, h is blurring function, x is original data • Deconvolution is obtaining x given y • Equivalent to solving a system of structured system of equations of the form Ax=b • A is usually very large Develop and implement deconvolution algorithms for image deblurring

  31. Present Status and future directions • Developed algorithms for solving deconvolution equations • Significant reductions in the computational expense • domain is not necessarily rectangular or continuous • Common in microscopy • Scans of different areas in a sample • Implement these algorithms for deblurring applications • Study other convolutions in microscopy • Geometric convolution

  32. Practical Example Systems • Deblurring function: hn1n2=exp(-(n12+n22)/104) • Substantial reduction in computational expense

  33. MICROCANTILEVER BASED DEVICES

  34. Micro-Cantilever Arrays • Multi-Cantilever arrays • Parallel probing • Higher throughputs • Coupling effects • Modeling and Analysis • Associated control design • Distributed control structure • Individual actuation and sensing • Fabrication and implementation issues Parallel and faster probing to obtain higher throughputs

  35. Micro-cantilever Sample Dynamics • Understanding micro-cantilever-sample dynamics • Essential to probing surfaces at nanoscales • Important for designing X-Y positioning systems • Studying complex dynamics • Dependence on model parameters • Complex dynamics shown analytically and observed in experiments • Important to identify avoidable conditions for imaging • Use them as test beds to study rich dynamics • Previous experience • Obtained a model to describe an AFM experiment • Proved and observed complex dynamics

  36. NANOFRICTION

  37. Nano-friction • Widely studied area • Fundamental understanding of interfacial phenomena • nanotribology • Study these phenomena in micro/nanostructures • Magnetic storage systems, nanolithography • System theoretic approach • Not explored • Obtain models to model friction at nanoscales • Explain observed phenomena • Use control tools to compensate for friction • Use observer based design • Friction compensation important in applications • nanolithography System theoretic modeling, analysis and compensation for nano-friction

  38. Nano-friction (cont’d.) • Preliminary work • Dynamic model for AFM • With friction model using JKR theory • Simulation of model show stick-slip motion • feedback laws to compensate stick-slip demonstrated in simulation • Substantial reduction of error in tracking • z-velocities were obtained from the model in the control design • Proposed work • Implement observer based design • Develop models to explain more observed phenomena

More Related