Retrieving the Quantitative Chemical Information at Nanoscale from Scanning Electron Microscope Energy Dispersive X-ray Measurements by Machine Learning

Jany, Benedykt R.; Janas, Arkadiusz

doi:10.1021/acs.nanolett.7b01789

Cited by 37 publications

(35 citation statements)

References 30 publications

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“…By examining the interplanar spacing of the atomic columns and their contrast in the HAADF STEM imaging, the nanowire composition is identified as an AuIn2 alloy 21 . AuIn2 nanowire's stoichiometry is also confirmed by the SEM/EDX measurement results which were analyzed by a newly developed method of chemical quantification based on the Machine Learning approach 32 .…”

Section: /31mentioning

confidence: 73%

Towards the understanding of the gold interaction with AIII-BV semiconductors at the atomic level

Jany

Janas²,

Piskorz³

et al. 2020

Nanoscale

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AIII-BV semiconductors have been considered for decades to be a promising material in overcoming the limitations of silicone semiconductor devices. One of the important aspects within AIII-BV semiconductor technology are gold-semiconductor interactions on the nanoscale, since Au is widely used to catalyze the growth of AIII-BV nanostructures.We report on the chemical interactions of Au atoms with AIII-BV semiconductor crystals by an investigation of the nanostructures formation in the process of thermally-induced Au self-assembly on various AIII-BV surfaces, and this by means of atomically resolved HAADF STEM measurements. We have found that the formation of nanostructures is a consequence of the surface diffusion and nucleation of adatoms produced by Au induced chemical reactions on AIII-BV semiconductor surfaces. Only for InSb crystal we have found that there is efficient diffusion of Au atoms into the bulk, which we 2/31 experimentally studied by Machine Learning HAADF STEM image quantification. The process of Au dissolution in InSb lattice has been additionally characterized by DFT calculations with inclusion of finite temperature effects. Furthermore, based on the stoichiometry of nanostructures grown, the effective number of Au atoms needed to release one AIII metal atom has been estimated. The experimental finding reveals a difference in the Au interactions with In-and Ga-based groups of AIII-BV semiconductors. Our comprehensive and systematic studies uncover the details of the Au interactions with the AIII-BV surface at the atomic level with chemical sensitivity.AIII-BV semiconductors due to their unique properties, such as high electron mobility and direct bandgap, are being considered material for the new generation of nanoscale electronic devices 1-4 .Recently, new technologies were developed such as template-assisted selective epitaxy (TASE) by IBM Zurich group 5 and epitaxial lift off (ELO) technique 6 to integrate AIII-BV with Si at the nanoscale. This will allow one to expand the use of AIII-BV crystals by extending the conventional Si-based technology for future AIII-BV/Si nanodevice fabrication 7 . For many applications the arrays of standing, vertically aligned AIII-BV nanowires grown on semiconductor surfaces are desired [8][9][10] . A device for efficient water splitting can be built from such AIII-BV nanowires arrays 11,12 . Also the AIII-BV nanowire LED device integrated on Si has been developed, with 3 orders of magnitude higher efficiency than the conventional device 13 . To control the physico-chemical properties of the devices local information on the atomic arrangement is necessary as has recently been shown by atomic resolution spectrum imaging in STEM 14 .The growth of such monocrystalline AIII-BV nanowires is mainly catalyzed by Au seeded nanoparticles 15 . Studies on the role of Au in the process of nanowires growth, in a closed system being in equilibrium, have been performed by Dick et al. 16 . They have shown that deposited Au is not inert with respect to AIII-BV material and i...

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Section: /31mentioning

confidence: 73%

Towards the understanding of the gold interaction with AIII-BV semiconductors at the atomic level

Jany

Janas²,

Piskorz³

et al. 2020

Nanoscale

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“…They extract local information on material structure based on statistical analysis of atomic neighborhoods based on Fourier Transform followed by clustering and multivariate algorithms like Principal Component Analysis (PCA), Independent Component Analysis (ICA). The Machine Learning approaches could be also successfully used for hyper-spectral EDX imaging in STEM (Rossouw et al, 2015) and in SEM (Jany et al, 2017b). The collected hyperspectral images in form of data cube i.e.…”

Section: Introductionmentioning

confidence: 99%

Automatic microscopic image analysis by moving window local Fourier Transform and Machine Learning

Jany

Janas

2020

Micron

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The microscopic image analysis is a tedious work which requires patience and time, usually done manually by microscopist after collecting the data. The results obtained in such a way might be biased by the human which performed the analysis. Here we introduce an approach of automatic image analysis, which is based on the locally applied Fourier Transform and Machine Learning methods. In this approach, the whole image is scanned by local moving window with defined size and the 2D Fourier Transform is calculated for each window. All the Local Fourier Transforms are fed into Machine Learning processing. Firstly, the number of components in the data is estimated from Principal Component Analysis (PCA) Scree Plot performed on the data. Secondly, the data are decomposed blindly by Non-Negative Matrix Factorization (NMF) into interpretable spatial maps (loadings) and corresponding Fourier Transforms (factors). Finally, the microscopic image is analyzed, the features on the image are automatically discovered, based on the local changes in Fourier Transform, without the human bias. The user selects only the size and movement of the scanning local window which defines the final analysis resolution. This automatic approach was

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“…Among the various BSS algorithms, the most common two applied to X-ray microanalysis are the independent component analysis (ICA) and the nonnegative matrix factorization (NMF), which both consider each decomposed component as a typical event occurring between the electron beam and the specimen [11][12][13]. The main difference between the two algorithms is that the NMF only allows additive combinations and prevents subtractions to force all components to be nonnegative, but the ICA allows both combinations and subtractions [14,15]. Even though the BSS can be performed independently, it usually requires the PCA as a preliminary step to make the original dataset less correlated and decide the ummixed dimension [14].…”

Section: Introductionmentioning

confidence: 99%

“…The main difference between the two algorithms is that the NMF only allows additive combinations and prevents subtractions to force all components to be nonnegative, but the ICA allows both combinations and subtractions [14,15]. Even though the BSS can be performed independently, it usually requires the PCA as a preliminary step to make the original dataset less correlated and decide the ummixed dimension [14]. There are more and more studies using the combination of PCA and BSS to distinguish different phases instead of the traditional elemental identification method [16,17].…”

Section: Introductionmentioning

confidence: 99%

Multivariate Statistical Analysis on a SEM/EDS Phase Map of Rare Earth Minerals

Teng

Gauvin

2020

Scanning

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The scanning electron microscope/X-ray energy dispersive spectrometer (SEM/EDS) system is widely applied to rare earth minerals (REMs) to qualitatively describe their mineralogy and quantitatively determine their composition. The performance of multivariate statistical analysis on the EDS raw dataset can enhance the efficiency and the accuracy of phase identification. In this work, the principal component analysis (PCA) and the blind source separation (BSS) algorithms were performed on an EDS map of a REM sample, assisting to achieve an efficient phase map analysis. The PCA significantly denoised the phase map and was used as a preprocessing step for the following BSS. The BSS separated the mixed EDS signals into a set of physically interpretable components, bringing convenience to the phase separation and identification. Through the comparison between the independent component analysis (ICA) and the nonnegative matrix factorization (NMF) algorithms, the NMF was confirmed to be more suitable for the EDS mapping analysis.

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Retrieving the Quantitative Chemical Information at Nanoscale from Scanning Electron Microscope Energy Dispersive X-ray Measurements by Machine Learning

Cited by 37 publications

References 30 publications

Towards the understanding of the gold interaction with AIII-BV semiconductors at the atomic level

Towards the understanding of the gold interaction with AIII-BV semiconductors at the atomic level

Automatic microscopic image analysis by moving window local Fourier Transform and Machine Learning

Multivariate Statistical Analysis on a SEM/EDS Phase Map of Rare Earth Minerals

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