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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
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R ESEARCH D IRECTIONS

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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
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Page 1: R ESEARCH  D IRECTIONS

RESEARCH DIRECTIONS

Srinivasa M. SalapakaLaboratory for Information and Decision Systems

Massachusetts Institute of Technology

Department of Mechanical EngineeringIowa State University

March 25, 2003

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Outline

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

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ROBUST BROADBAND NANOPOSITIONING

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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

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MOTIVATION

• Nanopositioning system

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

Needs ofCombinatorial

Chemistry

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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

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APPROACH

• Novel Device Architecture

• Novel paradigm for robustness, bandwidth and resolution

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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

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A Schematic of PROPOSED Nanoscope Head top

EOD, Laser

Laser to photodiode

Head

Laser from EOD

Microcantilever

Support Plate

X-Y-Z small rangenanopositioner

Large range nanopositioner

Mirror

MicrocantileverHolder

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Large Range Scanner

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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

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Results (cont’d.)hysteresis creep bandwidth

RepeatabilityReliability

tracking

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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

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Preview based control design

• Improve tracking performance For a priori known reference trajectories

feedforward controller in addition to feedback controller

To give desired input ud such that Gud(t)=xr(t)

+ -

Feed forward Controller

Plant

Anticipatory Control design for better tracking performance

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Preliminary Simulation Results

significant improvement in performance Substantial reduction in error

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Multi-Input Multi-Output Control Design

Gxx

Gyx Gyy

Gxy ¼ 0

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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

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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

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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

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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

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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

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CLUSTERING

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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

X

Combinatorially complex problem Interpret and design d(x,rj) Adapt and modify Deterministic

Annealing Algorithm Simulations

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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

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OBJECTIVE

Develop and adapt clustering algorithms for Combinatorial Discovery

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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

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EXAMPLE SYSTEM

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IMAGE RECONSTRUCTION

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MOTIVATION

• Blurred images in scanning probe microscopy The tip-geometry convolves with the sample to

provide a blurred image

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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

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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

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Practical Example Systems

• Deblurring function: hn1n2=exp(-(n12+n2

2)/104)

• Substantial reduction in computational expense

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MICROCANTILEVER BASED DEVICES

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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

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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

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NANOFRICTION

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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

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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