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Deep learning STEM-EDX tomography of nanocrystals

Abstract

Energy-dispersive X-ray spectroscopy (EDX) is often performed simultaneously with high-angle annular dark-field scanning transmission electron microscopy (STEM) for nanoscale physico-chemical analysis. However, high-quality STEM-EDX tomographic imaging is still challenging due to fundamental limitations such as sample degradation with prolonged scan time and the low probability of X-ray generation. To address this, we propose an unsupervised deep learning method for high-quality 3D EDX tomography of core–shell nanocrystals, which can be usually permanently dammaged by prolonged electron beam. The proposed deep learning STEM-EDX tomography method was used to accurately reconstruct Au nanoparticles and InP/ZnSe/ZnS core–shell quantum dots, used in commercial display devices. Furthermore, the shape and thickness uniformity of the reconstructed ZnSe/ZnS shell closely correlates with optical properties of the quantum dots, such as quantum efficiency and chemical stability.

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Fig. 1: Deep learning STEM-EDX tomography workflow.
Fig. 2: Commercially available QD reconstruction results.
Fig. 3: Proposed 3D STEM-EDX reconstruction results of two types of QDs.

Data availability

The data acquired before and after processing, used to make figures in this paper are publicly shared in the same repository where our code is uploaded (https://github.com/bispl-kaist/Deep-Learning-STEM-EDX-Tomography/ and https://zenodo.org/record/4294003#.X8FJkBNKjOS) under the directory ‘Deep_Learning_STEM-EDX_Tomography/Recon_data’.

Code availability

The codes used in this study are available from https://github.com/bispl-kaist/Deep-Learning-STEM-EDX-Tomography/ and https://zenodo.org/record/4294003#.X8FJkBNKjOS.

References

  1. 1.

    Alivisatos, A. P. Semiconductor clusters, nanocrystals, and quantum dots. Science 271, 933–937 (1996).

    Article  Google Scholar 

  2. 2.

    Loss, D. & DiVincenzo, D. P. Quantum computation with quantum dots. Phys. Rev. A 57, 120 (1998).

    Article  Google Scholar 

  3. 3.

    Won, Y.-H. et al. Highly efficient and stable inp/znse/zns quantum dot light-emitting diodes. Nature 575, 634–638 (2019).

    Article  Google Scholar 

  4. 4.

    Goldstein, J. I. et al. Scanning Electron Microscopy and X-ray Microanalysis (Springer, 2017).

  5. 5.

    Lepinay, K., Lorut, F., Pantel, R. & Epicier, T. Chemical 3D tomography of 28 nm high K metal gate transistor: STEM XEDS experimental method and results. Micron 47, 43–49 (2013).

    Article  Google Scholar 

  6. 6.

    Slater, T. J. et al. STEM-EDX tomography of bimetallic nanoparticles: a methodological investigation. Ultramicroscopy 162, 61–73 (2016).

    Article  Google Scholar 

  7. 7.

    Saghi, Z., Xu, X., Peng, Y., Inkson, B. & Möbus, G. Three-dimensional chemical analysis of tungsten probes by energy dispersive x-ray nanotomography. Appl. Phys. Lett. 91, 251906 (2007).

    Article  Google Scholar 

  8. 8.

    Zhong, Z., Goris, B., Schoenmakers, R., Bals, S. & Batenburg, K. J. A bimodal tomographic reconstruction technique combining EDS-STEM and HAADF-STEM. Ultramicroscopy 174, 35–45 (2017).

    Article  Google Scholar 

  9. 9.

    Gilbert, P. Iterative methods for the three-dimensional reconstruction of an object from projections. J. Theor. Biol. 36, 105–117 (1972).

    Article  Google Scholar 

  10. 10.

    Kang, E., Min, J. & Ye, J. C. A deep convolutional neural network using directional wavelets for low-dose X-ray CT reconstruction. Med. Phys. 44, e360–e375 (2017).

  11. 11.

    Chen, H. et al. Low-dose CT with a residual encoder-decoder convolutional neural network. IEEE Trans. Med. Imaging 36, 2524–2535 (2017).

    Article  Google Scholar 

  12. 12.

    Kang, E., Chang, W., Yoo, J. & Ye, J. C. Deep convolutional framelet denosing for low-dose CT via wavelet residual network. IEEE Trans. Med. Imaging 37, 1358–1369 (2018).

  13. 13.

    Jin, K. H., McCann, M. T., Froustey, E. & Unser, M. Deep convolutional neural network for inverse problems in imaging. IEEE Trans. Image Process. 26, 4509–4522 (2017).

    MathSciNet  Article  Google Scholar 

  14. 14.

    Han, Y. & Ye, J. C. Framing U-Net via deep convolutional framelets: application to sparse-view CT. IEEE Trans. Med. Imaging 37, 1418–1429 (2018).

    Article  Google Scholar 

  15. 15.

    Han, Y., Kang, J. & Ye, J. C. Deep learning reconstruction for 9-view dual energy CT baggage scanner. In Proc. Fifth International Conference on Image Formation in X-Ray Computed Tomography 407–410 (CT Meeting, 2018).

  16. 16.

    Wang, S. et al. Accelerating magnetic resonance imaging via deep learning. In 2016 IEEE 13th International Symposium on Biomedical Imaging 514–517 (IEEE, 2016)..

  17. 17.

    Hammernik, K. et al. Learning a variational network for reconstruction of accelerated MRI data. Magn. Reson. Med. 79, 3055–3071 (2018).

    Article  Google Scholar 

  18. 18.

    Lee, D., Yoo, J., Tak, S. & Ye, J. Deep residual learning for accelerated MRI using magnitude and phase networks. IEEE Trans. Biomed. Eng. 65, 1985–1995 (2018).

  19. 19.

    Han, Y. S., Yoo, J. & Ye, J. C. Deep learning with domain adaptation for accelerated projection reconstruction MR. Magn. Reson. Med. 80, 1189–1205 (2018).

  20. 20.

    Gong, K. et al. Iterative PET image reconstruction using convolutional neural network representation. IEEE Trans. Med. Imaging 38, 675–685 (2018).

  21. 21.

    Gong, K., Catana, C., Qi, J. & Li, Q. PET image reconstruction using deep image prior. IEEE Trans. Med. Imaging 38, 1655–1665 (2018).

  22. 22.

    Luchies, A. C. & Byram, B. C. Deep neural networks for ultrasound beamforming. IEEE Trans. Med. Imaging 37, 2010–2021 (2018).

    Article  Google Scholar 

  23. 23.

    Yoon, Y. H., Khan, S., Huh, J. & Ye, J. C. Efficient B-mode ultrasound image reconstruction from sub-sampled RF data using deep learning. IEEE Trans. Med. Imaging 38, 325–336 (2019).

    Article  Google Scholar 

  24. 24.

    Rivenson, Y. et al. Deep learning microscopy. Optica 4, 1437–1443 (2017).

    Article  Google Scholar 

  25. 25.

    Nehme, E., Weiss, L. E., Michaeli, T. & Shechtman, Y. Deep-STORM: super-resolution single-molecule microscopy by deep learning. Optica 5, 458–464 (2018).

    Article  Google Scholar 

  26. 26.

    Sinha, A., Lee, J., Li, S. & Barbastathis, G. Lensless computational imaging through deep learning. Optica 4, 1117–1125 (2017).

    Article  Google Scholar 

  27. 27.

    Donoho, D. L. Compressed sensing. IEEE Trans. Inf. Theory 52, 1289–1306 (2006).

    MathSciNet  Article  Google Scholar 

  28. 28.

    Ye, J. C., Han, Y. & Cha, E. Deep convolutional framelets: a general deep learning framework for inverse problems. SIAM J. Imag. Sci. 11, 991–1048 (2018).

    MathSciNet  Article  Google Scholar 

  29. 29.

    Ye, J. C. & Sung, W. K. Understanding geometry of encoder-decoder CNNs. In Proc. 36th International Conference on Machine Learning (eds Chaudhuri, K. & Salakhutdinov, R.) 7064–7073 (PMLR, 2019)..

  30. 30.

    Kim, Y. et al. Bright and uniform green light emitting InP/ZnSe/ZnS quantum dots for wide color gamut displays. ACS Appl. Nano Mater. 2, 1496–1504 (2019).

    Article  Google Scholar 

  31. 31.

    Lee, S. & Lee, C. High-density quantum dots composites and its photolithographic patterning applications. Polym. Adv. Technol. 30, 749–754 (2019).

    Article  Google Scholar 

  32. 32.

    Virieux, H. et al. InP/ZnS nanocrystals: coupling NMR and XPS for fine surface and interface description. J. Am. Chem. Soc. 134, 19701–19708 (2012).

    Article  Google Scholar 

  33. 33.

    Stein, J. L. et al. Probing surface defects of InP quantum dots using phosphorus Kα and Kβ X-ray emission spectroscopy. Chem. Mater. 30, 6377–6388 (2018).

    Article  Google Scholar 

  34. 34.

    Hahm, D. et al. Design principle for bright, robust, and color-pure InP/ZnSexS1–x/ZnS heterostructures. Chem. Mater. 31, 3476–3484 (2019).

    Article  Google Scholar 

  35. 35.

    Reid, K. R., McBride, J. R., Freymeyer, N. J., Thal, L. B. & Rosenthal, S. J. Chemical structure, ensemble and single-particle spectroscopy of thick-shell inp–znse quantum dots. Nano Lett. 18, 709–716 (2018).

    Article  Google Scholar 

  36. 36.

    Saxton, W. & Baumeister, W. The correlation averaging of a regularly arranged bacterial cell envelope protein. J. Microsc. 127, 127–138 (1982).

    Article  Google Scholar 

  37. 37.

    Çiçek, Ö., Abdulkadir, A., Lienkamp, S. S., Brox, T. & Ronneberger, O. 3D U-Net: learning dense volumetric segmentation from sparse annotation. In International Conference on Medical Image Computing and Computer-Assisted Intervention 424–432 (Springer, 2016).

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Acknowledgements

This research was supported by Samsung.

Author information

Affiliations

Authors

Contributions

J.C.Y. and E.L. supervised the project in conception and discussion. J.J., E.L. and J.C.Y. co-conceived the project. Y.H., E.C. and H.C. performed experiments for 3D tomography reconstruction. T.-G.K. and S.J. prepared the SAIT QD samples. J.L. performed TEM experiment and image reconstruction using SIRT. M.J., B.G.C. and H.G.K. performed TEM experiments. M.J. and J.J. performed image analysis. S.H. discussed the experiment and co-directed the project. Y.H., J.J., E.C., J.L., H.C., E.L. and J.C.Y. wrote manuscript.

Corresponding authors

Correspondence to Eunha Lee or Jong Chul Ye.

Ethics declarations

Competing interests

Y.H., E.C., H.C. and J.C.Y. were supported by a research grant from Samsung Electronics. J.J., J.L., M.J., T.-G.K., B.G.C., H.G.K., S.J., S.H. and E.L. are employees of the Samsung Electronics. The authors declare no other competing interests.

Additional information

Peer review informationNature Machine Intelligence thanks the anonymous reviewers for their contribution to the peer review of this work.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 3D reconstruction results of Au-Nanorod for HAADF and EDX measurement.

(a) HAADF ground-truth, and (b) proposed EDX reconstruction.

Extended Data Fig. 2 Comparison of reconstructions using conventional methods and the proposed method.

(a-c) Conventional reconstruction results for SIRT, l1 regularization, and total variation (TV) penalty with spatial 11 × 11 average filter to reduce the measurement noises and (d) our reconstruction results of commercially available QDs. (i) shows the 3D rendering results, and red, green, and blue boxes in (i) indicate (ii) xy- (iii) yz-, and (iv) xz-plane cut-view images, respectively. Yellow box illustrates the magnified images. Se, S, and Zn refer selenium, sulphur, and zinc components, respectively.

Extended Data Fig. 3 Conventional EDX images of sQD1 and sQD2.

(a) sQD1 and (b) sQD2 images acquired using Titan Cubed TEM 60-300 at 300kV, 8 μs dwell time. Se, S, and Zn refer selenium, sulphur, and zinc components, respectively.

Extended Data Fig. 4 Conventional method for analysing QD shape information in TEM images.

sQD images were acquired using Titan Cubed TEM 60-300 at 300 kV, 8 μs dwell time. Total number of particles are 338 and 264 for sQD1 and sQD2, respectively. The image analysis was done with Image J. To isolate particle and background, we calculate mean value of the total images to use as the threshold value. The diameter is calculated as equivalent circular area diameter using the number of counted pixels on each particle.

Extended Data Fig. 5 Photoluminescence characteristics of sQD1 and sQD2.

(a) and (b) are PL-PLE maps of sQD1 and sQD2 dispersed in toluene. Yellow arrows indicate the characteristic points of sQD2 compared sQD1. (c) Absorption and PL spectra for sQD1 and sQD2. (d) Comparison of their quantum efficiency.

Extended Data Fig. 6 SIRT 3D STEM-EDX reconstruction results with spatial 11 × 11 average filter.

(a) and (b) show the reconstruction results using sQD1 and sQD2, respectively. (i) shows the 3D rendering results, and red, green, and blue boxes in (i) indicate (ii) xy-, (iii) yz-, and (iv) xz-plane cut-view images, respectively. Yellow boxes illustrate the magnified images. Se, S, and Zn refer selenium, sulphur, and zinc components, respectively.

Extended Data Fig. 7 sQD1-1 thickness maps on spherical coordinates Φ and θ.

(a-c) sQD1-1 particle 2-D transformed element images; Red, green, and blue represent Se, S, and Zn, respectively. Se, S, and Zn thickness maps at 0≤Φ < 360 and − 90θ≤90. (d-f) Histogram of Se, S, and Zn thickness maps. Insets are projection images of Se, S, and Zn along z-axis. Colorbars are the number of accumulated pixels at each Φ and θ values.

Extended Data Fig. 8 sQD1-2 thickness maps on spherical coordinate Φ and θ.

(a-c) sQD1-2 particle 2-D transformed element images; Red, green, and blue represent Se, S, and Zn, respectively. Se, S, and Zn thickness maps at 0≤Φ < 360 and − 90θ≤90. (d-f) Histogram of Se, S, and Zn thickness maps. Insets are projection images of Se, S, and Zn along z-axis. Colorbars are the number of accumulated pixels at each Φ and θ values.

Extended Data Fig. 9 sQD2-1 thickness maps on spherical coordinate Φ and θ.

(a-c) sQD2-1 particle 2-D transformed element images; Red, green, and blue represent Se, S, and Zn, respectively. Se, S, and Zn thickness maps at 0≤Φ < 360 and − 90θ≤90. (d-f) Histogram of Se, S, and Zn thickness maps. Insets are projection images of Se, S, and Zn along z-axis. Colorbars are the number of accumulated pixels at each Φ and θ values.

Extended Data Fig. 10 sQD2-2 thickness maps on spherical coordinate Φ and θ.

(a-c) sQD2-2 particle 2-D transformed element images; Red, green, and blue represent Se, S, and Zn, respectively. Se, S, and Zn thickness maps at 0≤Φ < 360 and − 90θ≤90. (d-f) Histogram of Se, S, and Zn thickness maps. Insets are projection images of Se, S, and Zn along z-axis. Colorbars are the number of accumulated pixels at each Φ and θ values.

Supplementary information

Supplementary Information

Supplementary Figs. 1–13, discussion and Table 1.

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Han, Y., Jang, J., Cha, E. et al. Deep learning STEM-EDX tomography of nanocrystals. Nat Mach Intell 3, 267–274 (2021). https://doi.org/10.1038/s42256-020-00289-5

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