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Automated Scanning Probe Microscopy [A Step Closer to Atomically Precise Engineering?]

Automated Scanning Probe Microscopy [A Step Closer to Atomically Precise Engineering?]. Thursday 8 th July 2010. Richard Woolley , Julian Stirling, Prof. Philip Moriarty. Physics and Astronomy, The University of Nottingham, Nottingham, England Adrian Radocea

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Automated Scanning Probe Microscopy [A Step Closer to Atomically Precise Engineering?]

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  1. 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 Radocea College of Engineering, Cornell University, Ithaca, NY (USA)‏ Natalio Krasnogor Computer Science The University of Nottingham, Nottingham, England

  2. Automated Scanning Probe Microscopy

  3. The automation of science more than just static measurement Adam Routine measurement, DNA sequencing Distilling natural laws .[Schmidt et al Science 324 81 (2009)] Develops ’his’ own hypothesis and tests them [King, R. D et al Science 324 85 (2009)]

  4. Automated Scanning Probe Microscopy

  5. Z Scanning tip A X Y Sample surface Axis under direct (piezo) control Tools of the tradeThe scanning tunneling microscope A 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) I still can’t see what the tip looks like !! i G V

  6. Automated Scanning Probe Microscopy

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

  8. D. M. Eigler & E. K. Schweizer, Nature 344, 524 - 526 (1990) • Y. Sugimoto et al., Nature letters 446, 64 (2007).

  9. D. M. Eigler & E. K. Schweizer, Nature 344, 524 - 526 (1990) Hlaet al. Phys. Rev. Lett. 85, 2777–2780 (2000) • Y. Sugimoto et al., Nature letters 446, 64 (2007).

  10. D. M. Eigler & E. K. Schweizer, Nature 344, 524 - 526 (1990) C60 Hlaet al. Phys. Rev. Lett. 85, 2777–2780 (2000) D.L. Keeling et al. PRL 94, 146104 (2005) • Y. Sugimoto et al., Nature letters 446, 64 (2007).

  11. 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? I want to automate this! What’s the point… (Courtesy of A. Sweetman and P. Moriarty)

  12. ….more what’s the tip? Scanning tip What’s on the apex of my tip? i G A How do I know I have the best imaging parameters, V V V G i Sample surface “They’re just blobs Rich” This is important since the experimental observable, the image, is the result of the convolution between tip and surface.

  13. Automated Scanning Probe Microscopy

  14. Conditioning the tip Just considering STM In-situ Ex-situ Voltage pulsing (deliberate crash) Fine tuning (changing scan parameters)

  15. STM Imaging and tip conditioningThe human way Tip optimisation while imaging (HOPG) Tip type Image Magnified tip apex The image is the result of the convolution between surface and tip (and tip-surface interaction)

  16. Can we Automate Stage 1? Just considering STM In-situ Ex-situ Voltage pulsing (deliberate crash) Fine tuning (changing scan parameters)

  17. Stage 1, coarsely conditioning the probe a useful tool in itself A deterministic approach 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)

  18. Can we Automate Stage 2? ‘The automation of science’ In-situ Ex-situ Voltage pulsing (deliberate crash) Fine tuning (changing scan parameters)

  19. Vary the parameter space Target Image with suggested parameters I f V Tip sample interaction Resultant images Bad V V V f f f I I I Good Stage 2: Fine adjustment an intelligent SPM controller V The good, the bad and the ugly: The target dataset G i Image Dataset Good Bad 'Smart' SPM Deconvolve tip structure Tip Dataset Good Bad

  20. G V i G G G V V V i i i Machine Optimised Evolving a good image Cellular genetic algorithm G V i Starting image I’ve found a good one!

  21. 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 0.12 • Insets: 1x1nm2(a) before cGA, (b) optimised. • Stochastic selection of parameters, average RMI 0.01

  22. How good is it, how reliable is it? Is it comparative to a human operator? Automation:-It can make people happy

  23. Trigonal Honeycomb Honeycomb target Crude target Can we choose different tip states?HOPG in air 2.46Å α β 1.42Å 3.35Å I’ve found a good one of type 1 I’ve found a good one of type 2 [Mizes et al PRB 36 4491 (1987), Cisternas et al. PRB 79 205431 (2009)]

  24. Combining Stages 1 and 2 an intelligent SPM controller V G V G i i V G V G i i G G V V i i V G V G i i Start Manual set up: Target selection V G i Off-line protocol optimisation Target image, T, parameters, In Operator loads sample , tip and selects target image from database Start with best known parameters from database Human analysis, improved ‘Rules of Thumb’ Stage 1: Coarse conditioning Image analysis metrics Zoom, pulse, move Good Bad Stage 2: Optimisation Acquire STM image All image data is captured Cellular genetic algorithm breeds different individuals until the target image is obtained, Datamining Off-line image data base and associated imaging parameters High quality image V G i V G i

  25. Combining Stages 1 and 2 an intelligent SPM controller V G V G i i V G V G i i G G V V i i V G V G i i Start Manual set up: Target selection V G i Off-line protocol optimisation Target image, T, parameters, In Operator loads sample , tip and selects target image from database Start with best known parameters from database Human analysis, improved ‘Rules of Thumb’ Stage 1: Coarse conditioning Image analysis metrics Zoom, pulse, move Good Bad Stage 2: Optimisation Acquire STM image All image data is captured Cellular genetic algorithm breeds different individuals until the target image is obtained, Datamining Off-line image data base and associated imaging parameters High quality image V G i V G i

  26. Combining Stages 1 and 2 an intelligent SPM controller V G V G i i V G V G i i G G V V i i V G V G i i Start Manual set up: Target selection V G i Off-line protocol optimisation Target image, T, parameters, In Operator loads sample , tip and selects target image from database Start with best known parameters from database Human analysis, improved ‘Rules of Thumb’ Stage 1: Coarse conditioning Image analysis metrics Zoom, pulse, move Good Bad Stage 2: Optimisation Acquire STM image All image data is captured Cellular genetic algorithm breeds different individuals until the target image is obtained, Datamining Off-line image data base and associated imaging parameters High quality image V G i V G i

  27. Off line analysis and reference an intelligent SPM controller V G V G i i V G V G i i G G V V i i V G V G i i Start Manual set up: Target selection V G i Off-line protocol optimisation Target image, T, parameters, In Operator loads sample , tip and selects target image from database Start with best known parameters from database Human analysis, improved ‘Rules of Thumb’ Stage 1: Coarse conditioning Image analysis metrics Zoom, pulse, move Good Bad Stage 2: Optimisation Acquire STM image All image data is captured Cellular genetic algorithm breeds different individuals until the target image is obtained, Datamining Off-line image data base and associated imaging parameters High quality image V G i V G i

  28. Finding the correct parametersGetting the machine to learn a rule of thumb Plot the good image parameters Cluster the data to find which parameters (genotype) give the best image (phenotype)

  29. G V i Importantly it’s the paththe journey is more important than the destination c) d) Key Parameters Good image Start End Bad image Parameter Space

  30. Automation and Scanning Probe Technology

  31. Better ResolutionAFM Imaging Pentacene (IBM) Ball and Stick Model STM of Molecule AFM of Molecule The AFM tip was functionalised with a CO molecule, giving enhanced resolution over metal (Ag) termination AFM of Molecules • After, L.Gross et al. Science 325 1110 (2009)

  32. Thanks Julian Stirling and Prof. Philip Moriarty School of Physics and Astronomy, The University of Nottingham Dr. Natalio Krasnogor Computer Science, The University of Nottingham Prof. Paul Brown Department of Mechanical, Materials and Manufacturing Engineering, The University of Nottingham Adrian Radocea Department of Materials Science Engineering, Cornell University, Ithaca, New York Email: richard.woolley@nottingham.ac.uk Group website: www.nottingham.ac.uk/physics/research/nano

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