journal article·2020-03-19Open access

Imaging mechanism for hyperspectral scanning probe microscopy via Gaussian process modelling

Maxim Ziatdinov, Dohyung Kim, Sabine Neumayer, Rama K. Vasudevan, Liam Collins, Stephen Jesse, Mahshid Ahmadi, Sergei V. Kalinin

npj Computational Materials· Volume 6· Issue 1

Abstract

Abstract We investigate the ability to reconstruct and derive spatial structure from sparsely sampled 3D piezoresponse force microcopy data, captured using the band-excitation (BE) technique, via Gaussian Process (GP) methods. Even for weakly informative priors, GP methods allow unambiguous determination of the characteristic length scales of the imaging process both in spatial and frequency domains. We further show that BE data set tends to be oversampled in the spatial domains, with ~30% of original data set sufficient for high-quality reconstruction, potentially enabling faster BE imaging. At the same time, reliable reconstruction along the frequency domain requires the resonance peak to be within the measured band. This behavior suggests the optimal strategy for the BE imaging on unknown samples. Finally, we discuss how GP can be used for automated experimentation in SPM, by combining GP regression with non-rectangular scans.
Source: crossref · Quality: baseline

Research context

OpenAlex topics: Advanced Fluorescence Microscopy Techniques · Force Microscopy Techniques and Applications · Image Processing Techniques and Applications

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

References (36)

Show source-provided references
1. Binnig, G., Quate, C. F. & Gerber, C. Atomic force microscope. Phys. Rev. Lett. 56, 930–933 (1986).10.1103/PhysRevLett.56.930 ↗
2. Nonnenmacher, M., Oboyle, M. P. & Wickramasinghe, H. K. Kelvin probe force microscopy. Appl. Phys. Lett. 58, 2921–2923 (1991).10.1063/1.105227 ↗
3. Martin, Y. & Wickramasinghe, H. K. Magnetic imaging by force microscopy with 1000-A resolution. Appl. Phys. Lett. 50, 1455–1457 (1987).10.1063/1.97800 ↗
4. Rabe, U. & Arnold, W. Acoustic microscopy by atomic-force microscopy. Appl. Phys. Lett. 64, 1493–1495 (1994).10.1063/1.111869 ↗
5. Franke, K., Besold, J., Haessler, W. & Seegebarth, C. Modification and detection of domains on ferroelectric Pzt films by scanning force microscopy. Surf. Sci. 302, L283–L288 (1994).10.1016/0039-6028(94)91089-8 ↗
6. Kolosov, O., Gruverman, A., Hatano, J., Takahashi, K. & Tokumoto, H. Nanoscale visualization and control of ferroelectric domains by atomic force microscopy. Phys. Rev. Lett. 74, 4309–4312 (1995).10.1103/PhysRevLett.74.4309 ↗
7. Jesse, S., Baddorf, A. P. & Kalinin, S. V. Switching spectroscopy piezoresponse force microscopy of ferroelectric materials. Appl. Phys. Lett. 88, 062908 (2006).10.1063/1.2172216 ↗
8. Gerber, C. & Lang, H. P. How the doors to the nanoworld were opened. Nat. Nanotechnol. 1, 3–5 (2006).10.1038/nnano.2006.70 ↗
9. Garcia, R. & Perez, R. Dynamic atomic force microscopy methods. Surf. Sci. Rep. 47, 197–301 (2002).10.1016/S0167-5729(02)00077-8 ↗
10. Vasudevan, R. K., Jesse, S., Kim, Y., Kumar, A. & Kalinin, S. V. Spectroscopic imaging in piezoresponse force microscopy: new opportunities for studying polarization dynamics in ferroelectrics and multiferroics. MRS Commun. 2, 61–73 (2012).10.1557/mrc.2012.15 ↗
11. Balke, N. et al. Decoupling electrochemical reaction and diffusion processes in ionically-conductive solids on the nanometer scale. Acs Nano 4, 7349–7357 (2010).10.1021/nn101502x ↗
12. Jesse, S., Maksymovych, P. & Kalinin, S. V. Rapid multidimensional data acquisition in scanning probe microscopy applied to local polarization dynamics and voltage dependent contact mechanics. Appl. Phys. Lett. 93, 112903 (2008).10.1063/1.2980031 ↗
13. Jesse, S., Kalinin, S. V., Proksch, R., Baddorf, A. P. & Rodriguez, B. J. The band excitation method in scanning probe microscopy for rapid mapping of energy dissipation on the nanoscale. Nanotechnology 18, 435503 (2007).10.1088/0957-4484/18/43/435503 ↗
14. Platz, D., Tholen, E. A., Pesen, D. & Haviland, D. B. Intermodulation atomic force microscopy. Appl. Phys. Lett. 92, 3 (2008).10.1063/1.2909569 ↗
15. Belianinov, A., Kalinin, S. V. & Jesse, S. Complete information acquisition in dynamic force microscopy. Nat. Commun. 6, 6550 (2015).10.1038/ncomms7550 ↗
16. Collins, L. et al. G-mode magnetic force microscopy: separating magnetic and electrostatic interactions using big data analytics. Appl. Phys. Lett. 108, 193103 (2016).10.1063/1.4948601 ↗
17. Collins, L. et al. Full data acquisition in Kelvin probe force microscopy: mapping dynamic electric phenomena in real space. Sci. Rep. 6, 30557 (2016).10.1038/srep30557 ↗
18. Collins, L. et al. Breaking the time barrier in Kelvin probe force microscopy: fast free force reconstruction using the G-mode platform. ACS Nano 11, 8717–8729 (2017).10.1021/acsnano.7b02114 ↗
19. Somnath, S., Belianinov, A., Kalinin, S. V. & Jesse, S. Full information acquisition in piezoresponse force microscopy. Appl. Phys. Lett. 107, 263102 (2015).10.1063/1.4938482 ↗
20. Jesse, S. & Kalinin, S. V. Band excitation in scanning probe microscopy: sines of change. J. Phys. D Appl. Phys. 44, 464006 (2011).10.1088/0022-3727/44/46/464006 ↗
21. Forchheimer, D., Platz, D., Tholen, E. A. & Haviland, D. B. Model-based extraction of material properties in multifrequency atomic force microscopy. Phys. Rev. B 85, 7 (2012).10.1103/PhysRevB.85.195449 ↗
22. Jesse, S. & Kalinin, S. V. Principal component and spatial correlation analysis of spectroscopic-imaging data in scanning probe microscopy. Nanotechnology 20, 085714 (2009).10.1088/0957-4484/20/8/085714 ↗
23. Kannan, R. et al. Deep data analysis via physically constrained linear unmixing: universal framework, domain examples, and a community-wide platform. Adv. Struct. Chem. Imaging 4, 20 (2018).10.1186/s40679-018-0055-8 ↗
24. Su, Y. et al. DAEN: deep autoencoder networks for hyperspectral unmixing. IEEE Trans. Geosci. Remote Sens. 57, 4309–4321 (2019).10.1109/TGRS.2018.2890633 ↗
25. Nikiforov, M. P. et al. Functional recognition imaging using artificial neural networks: applications to rapid cellular identification via broadband electromechanical response. Nanotechnology 20, 405708 (2009).10.1088/0957-4484/20/40/405708 ↗
26. Ovchinnikov, O. S., Jesse, S., Bintacchit, P., Trolier-McKinstry, S. & Kalinin, S. V. Disorder identification in hysteresis data: recognition analysis of the random-bond-random-field Ising model. Phys. Rev. Lett. 103, 157203 (2009).10.1103/PhysRevLett.103.157203 ↗
27. Kumar, A. et al. Spatially resolved mapping of disorder type and distribution in random systems using artificial neural network recognition. Phys. Rev. B 84, 024203 (2011).10.1103/PhysRevB.84.024203 ↗
28. Lambert, B. A Student’s Guide to Bayesian Statistics 1st edn (SAGE Publications Ltd, 2018).
29. Kruschke, J. Doing Bayesian Data Analysis: A Tutorial with R, JAGS, and Stan 2nd edn (Academic Press, 2014).10.1016/B978-0-12-405888-0.00008-8 ↗
30. Gelman, A. et al. Bayesian Data Analysis (Chapman & Hall/CRC Texts in Statistical Science) 3rd edn (Chapman and Hall/CRC, 2013).
31. Rasmussen, C. E. & Williams, C. K. I. Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning) (The MIT Press, 2005).10.7551/mitpress/3206.001.0001 ↗
32. Bingham, E. et al. Pyro: deep universal probabilistic programming. J. Mach. Learn. Res. 20, 973–978 (2019).
33. Quiñonero-Candela, J. & Rasmussen, C. E. A unifying view of sparse approximate Gaussian process regression. J. Mach. Learn. Res. 6, 1939–1959 (2005).
34. Kim, C., Chandrasekaran, A., Jha, A. & Ramprasad, R. Active-learning and materials design: the example of high glass transition temperature polymers. MRS Commun. 9, 860–866 (2019).10.1557/mrc.2019.78 ↗
35. Lookman, T., Balachandran, P. V., Xue, D. & Yuan, R. Active learning in materials science with emphasis on adaptive sampling using uncertainties for targeted design. npj Computational Mater. 5, 21 (2019).10.1038/s41524-019-0153-8 ↗
36. Kusne, A. G. et al. On-the-fly machine-learning for high-throughput experiments: search for rare-earth-free permanent magnets. Sci. Rep. 4, 6367 (2014).10.1038/srep06367 ↗