Automated Scanning Probe Microscopy [A Step Closer to Atomically Precise Engineering?]
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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?)
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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?)
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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
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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
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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
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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
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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: [email protected]
Group website: www.nottingham.ac.uk/physics/research/nano