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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


Jan 06, 2016




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 - PowerPoint PPT Presentation

  • RESEARCH DIRECTIONSSrinivasa M. SalapakaLaboratory for Information and Decision SystemsMassachusetts Institute of TechnologyDepartment of Mechanical EngineeringIowa State University March 25, 2003

  • Outline

    Research DirectionsNanopositioningMicro-Cantilever DynamicsNanofrictionClustering AlgorithmsImage deblurring


  • MOTIVATIONNanopositioningHigh BandwidthHigh throughputsHigh throughput requirements in probing material surfacesBinding affinity between materials, other propertiesHigh speed requirements for studying biosystemsCell dynamics, probing living systemsFaster scanning requirements in various engineering applicationsUltra high density data reading and writingEnabling feature in many studies and applicationsStudies of cell dynamics require micro/nano-second imaging capabilities

    Ultrahigh precisionSpecifications are often in the angstrom regimeIn scanning probe technologies molecular and atomic forces are routinely probed

    RobustnessNecessary for reliability in view of Uncertainty in model and environmentDiverse users do not have the engineering expertise

  • MOTIVATION Nanopositioning system

    High precision (probing at nanoscale)High bandwidth (high throughputs)Robustness (reliability and repeatability)Needs ofCombinatorialChemistry


    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


    Novel Device Architecture

    Novel paradigm for robustness, bandwidth and resolution

  • Proposed designTwo stage scanningLarge ScansMotion possible by flexure based designSample-holders on steel platforms Heavy (smaller bandwidths)Actuation by stack-piezosLarge forces, large travels (100 m)Small Scans Cylindrical PiezoactuatorsSample kept on actuator itselfSmaller travels (2 m)Lighter (higher bandwidths)Integrate the twoPut the small scanner on top of large scanner

  • A Schematic of PROPOSED Nanoscope

  • Large Range Scanner


    Developed a precise paradigm to address:High BandwidthHigh ResolutionRobustnessModern control toolsModel the plantQuantify and characterize the challengesDesign feedback laws Practically eliminated hysteresis and creepObtained 60-70 times improvement in the bandwidth over current popular systemsSubstantial improvement in the reliability and repeatability

  • Results (contd.)hysteresiscreepbandwidthRepeatabilityReliabilitytracking

  • Results Large ScannersIdentified and addressed design challenges on bandwidth, precision and robustnessPiezo actuation is predominant; hysteresis and creep nonlinearities, design constraintsSensors can deteriorate open loop performance

    Employed modern control tools to address these challenges and achievedPerformancecontrollers to achieve the desired tradeoff between resolution and bandwidthRobustnessBy addressing model uncertainties

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

  • Preliminary Simulation Results significant improvement in performanceSubstantial reduction in error

  • Multi-Input Multi-Output Control DesignGxxGyxGyyGxy 0

  • Multi-Input Multi-Output Control Design

    MIMO designSignificant coupling effectsGyx greater than Gyy in some frequencies Carry out control design for the MIMO modelGlover McFarlane, Nominal and Robust H1

    Multi-objective designActuation constraintsSpecified by H1 normResolution specificationsaddressed by H2 norm

    Control Design for plant model that includes X-Y coupling

  • Integration into the nanoscopeIntegrate the probing head with the positioning systemSample holder capable of moving in Z directionControl of tip-sample separation

    MIMO control design for positioner and cantilever system (3 3 model)Account for tip-sample interactionsNonlinear modelsObserver based control designz-displacements are measured but velocities are not measuredObservers useful for compensation designs for nanofriction

    Control Design for plant model that include positioning (X-Y) and probing (Z) aspects

  • Short Range ScannerFor high bandwidth Low mass essentialCylindrical piezos scanner cum actuatorCan be run open-loopInverse dynamic schemes Inverse hysteresis models Alternatively use closed loop control loop designDesign/implement sensors for detection of lateral motionEmploy the control design procedure as done for large range scannerSmaller lighter scanner implies faster scanning

  • Lateral motion sensors for AFMPrevious experienceDesigned sensors for shell piezos (J scanner in an AFM)Designed sensors based on optical levers

    Used them for feedbackLoop shaping control lawsObtained substantial improvement in the performanceResolution in order of few nm (1kHz)Bandwidth improvement of over 20 times

  • Another untried approachBuild a new nanoscope with control design in mindMake small scannersLighter and therefore high resonant frequenciesFaster scannersBigger coupling effectsMore burden on control design

    UpshotSimpler device designMore emphasis on control designAchieve higher bandwidthsShift the emphasis from device design to control design and achieve faster scanning rates


  • What Is Clustering?Clustering Separation of set of objects into groups such that objects in one group are more similar than those in otherfind the optimal partition {Rj} of the domain and the allocation of representative locations Combinatorially complex problemInterpret and design d(x,rj)Adapt and modify Deterministic Annealing AlgorithmSimulations


    Chemoinformatics, Combinatorial DiscoverySearch by elimination through a chemical space for a backbone compound (drug discovery)Enormous number of possible molecular combinationsRequires clustering algorithms to narrow the search

    Essential in data mining, data compression, facility location, machine learning

  • OBJECTIVEDevelop and adapt clustering algorithms for Combinatorial Discovery

  • Present status and future directionsPartition a large space optimally into a given number of cells and specify representative locations under constraintsSimilar to dividing chemical space into clusters with representative elements

    Developed fast algorithms under which a new class of problems were for the first time identifiedprecise mathematical formulations were providedAlgorithms developed that are fastThe developed algorithms utilized on real life systems



  • MOTIVATIONBlurred images in scanning probe microscopyThe tip-geometry convolves with the sample to provide a blurred image

  • ObjectiveDeblurred using deconvolution methodsModeled as convolution equation: y=h*xy is observed data, h is blurring function, x is original dataDeconvolution is obtaining x given yEquivalent to solving a system of structured system of equations of the form Ax=bA is usually very large

    Develop and implement deconvolution algorithms for image deblurring

  • Present Status and future directionsDeveloped algorithms for solving deconvolution equationsSignificant reductions in the computational expense domain is not necessarily rectangular or continuousCommon in microscopyScans of different areas in a sample Implement these algorithms for deblurring applicationsStudy other convolutions in microscopyGeometric convolution

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


  • Micro-Cantilever Arrays

    Multi-Cantilever arraysParallel probing Higher throughputsCoupling effectsModeling and AnalysisAssociated control designDistributed control structureIndividual actuation and sensingFabrication and implementation issuesParallel and faster probing to obtain higher throughputs

  • Micro-cantilever Sample Dynamics

    Understanding micro-cantilever-sample dynamicsEssential to probing surfaces at nanoscalesImportant for designing X-Y positioning systems

    Studying complex dynamicsDependence on model parametersComplex dynamics shown analytically and observed in experimentsImportant to identify avoidable conditions for imagingUse them as test beds to study rich dynamics

    Previous experienceObtained a model to describe an AFM experimentProved and observed complex dynamics


  • Nano-frictionWidely studied areaFundamental understanding of interfacial phenomenananotribologyStudy these phenomena in micro/nanostructuresMagnetic storage systems, nanolithographySystem theoretic approachNot exploredObtain models to model friction at nanoscalesExplain observed phenomenaUse control tools to compensate for frictionUse observer based designFriction compensation important in applicationsnanolithographySystem theoretic modeling, analysis and compensation for nano-friction

  • Nano-friction (contd.)Preliminary workDynamic model for AFMWith friction model using JKR theorySimulation of model show stick-slip motionfeedback laws to compensate stick-slip demonstrated in simulation Substantial reduction of error in trackingz-velocities were obtained from the model in the control design

    Proposed workImplement observer based designDevelop models to explain more observed phenomena


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