Reduced electron exposure for energy-dispersive spectroscopy using dynamic sampling

Zhang, Yan; Godaliyadda, G. M.  Dilshan; Ferrier, Nicola; Gulsoy, Emine B.; Bouman, Charles A.; Phatak, Charudatta

doi:10.1016/j.ultramic.2017.10.015

Title: Reduced electron exposure for energy-dispersive spectroscopy using dynamic sampling

Journal Article · Mon Oct 23 00:00:00 EDT 2017 · Ultramicroscopy

DOI:https://doi.org/10.1016/j.ultramic.2017.10.015· OSTI ID:1416973

Zhang, Yan ^[1]; Godaliyadda, G. M. Dilshan ^[2]; Ferrier, Nicola ^[3]; Gulsoy, Emine B. ^[4]; Bouman, Charles A. ^[5]; Phatak, Charudatta ^[1]

Argonne National Lab. (ANL), Argonne, IL (United States). Materials Science Division
Argonne National Lab. (ANL), Argonne, IL (United States). Materials Science Division ; Purdue Univ., West Lafayette, IN (United States). ECE Department
Argonne National Lab. (ANL), Argonne, IL (United States). Mathematics and Computer Science Division
Northwestern Univ., Evanston, IL (United States). Department of Materials Science and Engineering
Purdue Univ., West Lafayette, IN (United States). ECE Department

Analytical electron microscopy and spectroscopy of biological specimens, polymers, and other beam sensitive materials has been a challenging area due to irradiation damage. There is a pressing need to develop novel imaging and spectroscopic imaging methods that will minimize such sample damage as well as reduce the data acquisition time. The latter is useful for high-throughput analysis of materials structure and chemistry. Here, in this work, we present a novel machine learning based method for dynamic sparse sampling of EDS data using a scanning electron microscope. Our method, based on the supervised learning approach for dynamic sampling algorithm and neural networks based classification of EDS data, allows a dramatic reduction in the total sampling of up to 90%, while maintaining the fidelity of the reconstructed elemental maps and spectroscopic data. In conclusion, we believe this approach will enable imaging and elemental mapping of materials that would otherwise be inaccessible to these analysis techniques.

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Research Organization:: Argonne National Laboratory (ANL), Argonne, IL (United States)

Sponsoring Organization:: USDOE Office of Science (SC), Basic Energy Sciences (BES); Argonne National Laboratory - Laboratory Directed Research and Development (LDRD)

Grant/Contract Number:: AC02-06CH11357

OSTI ID:: 1416973

Alternate ID(s):: OSTI ID: 1549382

Journal Information:: Ultramicroscopy, Vol. 184, Issue PB; ISSN 0304-3991

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

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Cited by: 8 works

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References (15)

Atomic-resolution chemical mapping using energy-dispersive x-ray spectroscopy D’Alfonso, A. J.; Freitag, B.; Klenov, D. Physical Review B, Vol. 81, Issue 10 https://doi.org/10.1103/PhysRevB.81.100101	journal	March 2010
Single atom identification by energy dispersive x-ray spectroscopy Lovejoy, T. C.; Ramasse, Q. M.; Falke, M. Applied Physics Letters, Vol. 100, Issue 15 https://doi.org/10.1063/1.3701598	journal	April 2012
Communications-Inspired Projection Design with Application to Compressive Sensing Carson, William R.; Chen, Minhua; Rodrigues, Miguel R. D. SIAM Journal on Imaging Sciences, Vol. 5, Issue 4 https://doi.org/10.1137/120878380	journal	January 2012
Optimization of k -space trajectories for compressed sensing by Bayesian experimental design: Bayesian Optimization of k -Space Trajectories Seeger, Matthias; Nickisch, Hannes; Pohmann, Rolf Magnetic Resonance in Medicine, Vol. 63, Issue 1 https://doi.org/10.1002/mrm.22180	journal	October 2009
A Bayesian approach to targeted experiment design Vanlier, J.; Tiemann, C. A.; Hilbers, P. A. J. Bioinformatics, Vol. 28, Issue 8 https://doi.org/10.1093/bioinformatics/bts092	journal	February 2012
An adaptive multirate algorithm for acquisition of fluorescence microscopy data sets Merryman, T. E.; Kovacevic, J. IEEE Transactions on Image Processing, Vol. 14, Issue 9 https://doi.org/10.1109/TIP.2005.855861	journal	September 2005
A general regression neural network Specht, D. F. IEEE Transactions on Neural Networks, Vol. 2, Issue 6 https://doi.org/10.1109/72.97934	journal	January 1991
Least Squares Support Vector Machine Classifiers Suykens, J. A. K.; Vandewalle, J. Neural Processing Letters, Vol. 9, Issue 3, p. 293-300 https://doi.org/10.1023/A:1018628609742	journal	June 1999
Reducing the Dimensionality of Data with Neural Networks Hinton, G. E. Science, Vol. 313, Issue 5786 https://doi.org/10.1126/science.1127647	journal	July 2006
Deep learning LeCun, Yann; Bengio, Yoshua; Hinton, Geoffrey Nature, Vol. 521, Issue 7553 https://doi.org/10.1038/nature14539	journal	May 2015
A Perspective on Deep Imaging Wang, Ge IEEE Access, Vol. 4 https://doi.org/10.1109/ACCESS.2016.2624938	journal	January 2016
Deep learning for regulatory genomics Park, Yongjin; Kellis, Manolis Nature Biotechnology, Vol. 33, Issue 8 https://doi.org/10.1038/nbt.3313	journal	August 2015
A Framework for Dynamic Image Sampling Based on Supervised Learning Godaliyadda, G. M. Dilshan P.; Ye, Dong Hye; Uchic, Michael D. IEEE Transactions on Computational Imaging, Vol. 4, Issue 1 https://doi.org/10.1109/TCI.2017.2777482	journal	March 2018
Dynamic X-ray diffraction sampling for protein crystal positioning Scarborough, Nicole M.; Godaliyadda, G. M. Dilshan P.; Ye, Dong Hye Journal of Synchrotron Radiation, Vol. 24, Issue 1 https://doi.org/10.1107/S160057751601612X	journal	January 2017
Three-dimensional analysis of particle coarsening in high volume fraction solid–liquid mixtures Rowenhorst, D. J.; Kuang, J. P.; Thornton, K. Acta Materialia, Vol. 54, Issue 8 https://doi.org/10.1016/j.actamat.2005.12.038	journal	May 2006

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Progress toward autonomous experimental systems for alloy development Boyce, Brad L.; Uchic, Michael D. MRS Bulletin, Vol. 44, Issue 4 https://doi.org/10.1557/mrs.2019.75	journal	April 2019

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