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Automated Scanning Probe Microscopy

[A Step Closer to Atomically Precise Engineering?]

Thursday 8th July 2010

Richard Woolley, Julian Stirling, Prof. Philip Moriarty.Physics and Astronomy, The University of Nottingham, Nottingham, England

Adrian RadoceaCollege of Engineering, Cornell University, Ithaca, NY (USA)

Natalio KrasnogorComputer Science The University of Nottingham, Nottingham, England

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Automated Scanning Probe Microscopy

How computers and automated systems can help us

Scanning probe microscope (briefly)

Atomically Precise Engineering (briefly)

Can we automate the scanning probe?

What's the point?(How does it affect me?)

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The automation of sciencemore than just static

measurement

Routine measurement, DNA sequencing

Distilling natural laws.[Schmidt et al Science 324 81 (2009)]

Adam Develops ’his’ own hypothesis and tests them[King, R. D et al Science 324 85 (2009)]

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Automated Scanning Probe Microscopy

How computers and automated systems can help us

Scanning probe microscope (briefly)

Atomically Precise Engineering (briefly)

Can we automate the scanning probe?

What's the point?(How does it affect me?)

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Tools of the trade The scanning

tunneling microscopeA Scanning tunneling microscope maps the topographical and/or electronic surface features by scanning an atomically sharp tip within nm’s of the surface

it exp(−2kd)∝

AScanning tip

Sample surface

X

Z

Y

Axis under direct (piezo) control

G i

V

I still can’t see what the tip looks like !!

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Automated Scanning Probe Microscopy

How computers and automated systems can help us

Scanning probe microscope (briefly)

Atomically Precise Engineering (briefly)

Can we automate the scanning probe?

What's the point?(How does it affect me?)

7

D. M. Eigler & E. K. Schweizer, Nature 344, 524 - 526 (1990)

Image and manipulate with atomic precision

Identify the composition

Perform Local chemistry

Understand Dynamics of

motion

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D. M. Eigler & E. K. Schweizer, Nature 344, 524 - 526 (1990)

Y. Sugimoto et al., Nature letters 446, 64 (2007).

Image and manipulate with atomic precision

Identify the composition

Perform Local chemistry

Understand Dynamics of

motion

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D. M. Eigler & E. K. Schweizer, Nature 344, 524 - 526 (1990)

Y. Sugimoto et al., Nature letters 446, 64 (2007).

Hla et al. Phys. Rev. Lett. 85, 2777–2780 (2000)

Image and manipulate with atomic precision

Identify the composition

Perform Local chemistry

Understand Dynamics of

motion

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D. M. Eigler & E. K. Schweizer, Nature 344, 524 - 526 (1990)

D.L. Keeling et al. PRL 94, 146104 (2005)Y. Sugimoto et al., Nature letters 446, 64 (2007).

Hla et al. Phys. Rev. Lett. 85, 2777–2780 (2000)

Image and manipulate with atomic precision

Identify the composition

Perform Local chemistry

Understand Dynamics of

motion

C60

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Image and manipulate with atomic precision

Identify the composition

Perform Local chemistry

Understand Dynamics of

motion

So why if we can manipulate atoms and molecules (even perform local chemistry) can’t we simply record the physical parameters of the process and repeat?

(Courtesy of A. Sweetman and P. Moriarty)

I want to automate this!

What’s the point…

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….more what’s the tip?

A

V

Scanning tip

Sample surface

What’s on the apex of my tip?

How do I know I have the best imaging parameters,

GiV

Gi

V

This is important since the experimental observable, the image, is the result of the convolution between tip and surface.

“They’re just blobs Rich”

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Automated Scanning Probe Microscopy

How computers and automated systems can help us

Scanning probe microscope (briefly)

Atomically Precise Engineering (briefly)

Can we automate the scanning probe?

What's the point?(How does it affect me?)

14

Conditioning the tip

Just considering STM

Ex-situ

In-situ

Voltage pulsing (deliberate crash)

Fine tuning (changing scan

parameters)

Chemical process/ annealing

Stage 1: Coarse Conditioning

Stage 2: Fine adjustment

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STM Imaging and tip conditioning The human way

Tip type ImageTip optimisation while imaging (HOPG)

The image is the result of the convolution between surface and tip (and tip-surface interaction)

Magnified tip apex

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Can we Automate Stage 1?Just

considering STM

Ex-situ

In-situ

Voltage pulsing (deliberate crash)

Fine tuning (changing scan

parameters)

Chemical process/ annealing

Stage 1: Coarse Conditioning

Stage 2: Fine adjustment

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Stage 1, coarsely conditioning the probe

a useful tool in itself

Streaky Image. Executing cleaning pulse

Cloudy Image. Executing cleaning pulse

Flat Surface. Zooming in to 50nm

Flat Surface. Zooming in to 20nm

Constant Atomic resolution. Zooming in to 4nm

Poor Atomic resolution.Rescanning

Consistent fair atomic resolution. Stage 1 complete. Time elapsed: 1010.1902 (~17mins)

A deterministic approach

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Can we Automate Stage 2? ‘The automation of

science’

Ex-situ

In-situ

Voltage pulsing (deliberate crash)

Fine tuning (changing scan

parameters)

Chemical process/ annealing

Stage 1: Coarse Conditioning

Stage 2: Fine adjustment

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Stage 2: Fine adjustment an intelligent SPM controller

'Smart' SPM

The good, the bad and the ugly: The target dataset

Imag

e D

atas

et

Good

Good

Bad

Bad

Tip

Dat

aset

Deconvolve tip structure

Target Image with

suggested parameters

IV f

Resultant imagesGood

IV f

Bad

IV f

Vary the parameter

space

IV fTip sample interaction

G i V

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I’ve found a good one!

Evolving a good image

Cellular genetic algorithm

Starting image Machine Optimised

GiV

GiV

GiV

GiV

GiV

Do I really need a cGA? Would a

stochastic selection be just as good?

• Standard deviation is from the ‘noise’ of the GA

• RMI average 0.12Insets: 1x1nm2

(a) before cGA, (b) optimised.

• Stochastic selection of parameters, average RMI 0.01

How good is it, how reliable is it? Is it comparative to a

human operator?

Micro-scopist

Ave. RMI

Change in RMI/min

i-SPM 0.20 7.1Human 0.09 2.6

Automation:-It can make people happy

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Can we choose different tip states?

HOPG in air

3.35Å

2.46Å

1.42Å

Trigonal Honeycomb

Crude target Honeycomb target

I’ve found a good one of type 1

I’ve found a good one of type 2

α

β

[Mizes et al PRB 36 4491 (1987), Cisternas et al. PRB 79 205431 (2009)]

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Combining Stages 1 and 2 an intelligent SPM controller

Manual set up: Target selection

Stage 2: Optimisation

Off-line protocol

optimisation

Stage 1: Coarse conditioningStart with best known parameters from database

Target image, T, parameters, In

GiV

Good

GiV iV G All image data is

captured

Acquire STM

image

High quality image

Bad

Off-line image data base and associated imaging

parameters

Image analysis metrics

Zoom, pulse, move

Human analysis, improved ‘Rules of Thumb’

Operator loads sample , tip and selects target image

from database

Datamining Cellular genetic algorithm

breeds different individuals until the target

image is obtained,

Start

GiVGiV

GiVGiV

iVGiV

GiVGiV

G

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Combining Stages 1 and 2 an intelligent SPM controller

Manual set up: Target selection

Stage 2: Optimisation

Off-line protocol

optimisation

Stage 1: Coarse conditioningStart with best known parameters from database

Target image, T, parameters, In

GiV

Good

GiV iV G All image data is

captured

Acquire STM

image

High quality image

Bad

Off-line image data base and associated imaging

parameters

Image analysis metrics

Zoom, pulse, move

Human analysis, improved ‘Rules of Thumb’

Operator loads sample , tip and selects target image

from database

Datamining Cellular genetic algorithm

breeds different individuals until the target

image is obtained,

Start

GiVGiV

GiVGiV

iVGiV

GiVGiV

G

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Combining Stages 1 and 2 an intelligent SPM controller

Manual set up: Target selection

Stage 2: Optimisation

Off-line protocol

optimisation

Stage 1: Coarse conditioningStart with best known parameters from database

Target image, T, parameters, In

GiV

Good

GiV iV G All image data is

captured

Acquire STM

image

High quality image

Bad

Off-line image data base and associated imaging

parameters

Image analysis metrics

Zoom, pulse, move

Human analysis, improved ‘Rules of Thumb’

Operator loads sample , tip and selects target image

from database

Datamining Cellular genetic algorithm

breeds different individuals until the target

image is obtained,

Start

GiVGiV

GiVGiV

iVGiV

GiVGiV

G

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Off line analysis and reference an intelligent SPM controller

Manual set up: Target selection

Stage 2: Optimisation

Off-line protocol

optimisation

Stage 1: Coarse conditioningStart with best known parameters from database

Target image, T, parameters, In

GiV

Good

GiV iV G All image data is

captured

Acquire STM

image

High quality image

Bad

Off-line image data base and associated imaging

parameters

Image analysis metrics

Zoom, pulse, move

Human analysis, improved ‘Rules of Thumb’

Operator loads sample , tip and selects target image

from database

Datamining Cellular genetic algorithm

breeds different individuals until the target

image is obtained,

Start

GiVGiV

GiVGiV

iVGiV

GiVGiV

G

Finding the correct parametersGetting the machine

to learn a rule of thumb

Cluster the data to find which parameters (genotype) give the best image (phenotype)

Plot the good image parameters

Importantly it’s the paththe journey is more important than

the destination

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d)c)

GiV

Start End

Good image

Bad imageParameter Space

Key Parameters

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Automation and Scanning Probe Technology

Applying automation to STM as an aid

Repeatable tip fabrication, • Image metrics and standardisation• Repeated tests, catalytic systems

Moreover, the system can evolve its own useful nanostructure, what else can it evolve?• Genetic Algorithms have been used in Pharma and Analogue Electronics to

design molecules and systems• What about if we allow the system to build what it wants?

Why to I care?...

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Better ResolutionAFM Imaging Pentacene (IBM)

Ball and Stick Model

After, L.Gross et al. Science 325 1110 (2009)

STM of Molecule

AFM of Molecule

AFM of Molecules

The AFM tip was functionalised with a CO

molecule, giving enhanced resolution over metal (Ag)

termination

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Thanks

Julian Stirling and Prof. Philip MoriartySchool of Physics and Astronomy, The University of Nottingham

Dr. Natalio KrasnogorComputer Science, The University of Nottingham

Prof. Paul BrownDepartment of Mechanical, Materials and Manufacturing

Engineering, The University of Nottingham

Adrian RadoceaDepartment of Materials Science Engineering, Cornell University,

Ithaca, New York

Email: richard.woolley@nottingham.ac.uk

Group website: www.nottingham.ac.uk/physics/research/nano