J. Semicond. > 2022, Volume 43 > Issue 8 > 081001, doi: 10.1088/1674-4926/43/8/081001
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REVIEWS

Review in situ transmission electron microscope with machine learning

Zhiheng Cheng, Chaolun Wang, Xing Wu and Junhao Chu

+ Author Affiliations

 Corresponding author: Chaolun Wang, clwang@cee.ecnu.edu.cn; Xing Wu, xwu@cee.ecnu.edu.cn

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Abstract: Advanced electronic materials are the fundamental building blocks of integrated circuits (ICs). The microscale properties of electronic materials (e.g., crystal structures, defects, and chemical properties) can have a considerable impact on the performance of ICs. Comprehensive characterization and analysis of the material in real time with high-spatial resolution are indispensable. In situ transmission electron microscope (TEM) with atomic resolution and external field can be applied as a physical simulation platform to study the evolution of electronic material in working conditions. The high-speed camera of the in situ TEM generates a high frame rate video, resulting in a large dataset that is beyond the data processing ability of researchers using the traditional method. To overcome this challenge, many works on automated TEM analysis by using machine-learning algorithm have been proposed. In this review, we introduce the technical evolution of TEM data acquisition, including analysis, and we summarize the application of machine learning to TEM data analysis in the aspects of morphology, defect, structure, and spectra. Some of the challenges of automated TEM analysis are given in the conclusion.

Key words: electron microscopy, machine learning, in situ, image analysis, semiconductor



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Fig. 1.  (Color online) The evolution of TEM data acquisition and processing.

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Fig. 2.  (Color online) Publications from the Web of Science with the keywords of TEM and machine learning.

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Fig. 3.  (Color online) Application of artificial intelligence for high-efficient processing of various TEM datasets. FCN, CNN, and UDN are short for fully convolution network, convolutional neural network, and unsupervised deep network, respectively.

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Fig. 4.  (Color online) The introduction of a neural network in the analysis of atomic defects[72]. (a) Schematic architecture of a CNN with an encoder-decoder structure. (b) Location of carbon atoms of graphene. (c) The extracted dopant Si atoms and, in this way, the classification between 3-fold and 4-fold Si defects is conducted. (d) The evolution of the same Si dopant on the time scale. (e) Classification of defect types. (f) Extraction of the defect from STEM images.

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Fig. 5.  (Color online) Introduction of CNN-based algorithm for atomic-scale identification of point and line defects[61, 64]. (a) A CNN as a denoiser process. (b, c) Comparison of the signal-to-noise between the unprocessed and processed images. (d) Defect mapping of STEM image. (e) The statistics of Se vacancies and V dopants change according to the electron beam irradiation time. The scale bar is 5 nm. (f) The architecture of U-Net. (g, h) The border recognition of a nanopore.

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Fig. 6.  (Color online) Introduction of two machine learning based methods in morphology analysis of gold particles[63, 68, 70]. (a, b) A complete unsupervised clustering algorithm utilized in classification of gold nanorods. (c) A GA containing various image analysis methods as genes to explore the morphological characteristics of NPs. (d) NPs segmented by U-Net with boundaries. (e) The intensity of the four types of NPs and the background in the TEM images explored by U-Net. (f) Boundaries of NPs colored to their local etching rates and the etching time indicate the height. (g) The three etching stages defined by the relationships between the etching rate and curvature.

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Fig. 7.  (Color online) Application of machine learning based methods in the analysis of atomic column heights. (a) A CNN illustration as an example of the supervised network utilized in the analysis. (b) Comparison between ground truth and prediction after regression and classification on STEM images. (c) Results of the network with various defocus values and electron dose. (d) The sub-unit cell calculated and mapped from a 4D-STEM dataset. (e) Comparison between c-CNN and r-CNN measurement and HAADF estimation.

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Fig. 8.  (Color online) Application of CNN in structure analysis. (a) The workflow of deploying a neural network model to automatically classify structure. (b–d) Two-dimensional diffraction patterns of different crystal structures are divided into eight classes in SAED. (e) An illustration of a CNN(LeNet-5) with a computer-simulated training dataset. (f) Comparison between manual and automatic classification. (g) Correlation matrix of the results of the two methods.

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Fig. 9.  (Color online) Introduction of the use of a neural network to explore the correlative laws between local geometries and plasmon responses[65]. (a) The im2spec network, in which subimages are taken as features and spectra are taken as targets. (b) The spec2im network, which is trained with HAADF spatial descriptors and corresponding spectral descriptors, similar to the im2spec network. However, the spectra are used as features and subimages are used as targets.

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Fig. 10.  (Color online) The work flow of the 3D construction of STEM energy-dispersive X-ray spectroscopy (STEM-EDX) by deep learning[67]. (a) Collection of raw EDX experimental maps. (b) A denoising network to improve the noisy EDX maps. (c) Unsupervised deep network to generate high-quality 3D reconstruction.

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Table 1.   The main parameters of TEM data analysis that is based on machine learning of different types of material properties.

ClassificationAlgorithmData typeTraining
Dataset		Testing
Dataset		AccuracyRef.
DefectFCN (weakly supervised)STEMSimulationExperiment~97%[64]
CNN and U-Net (supervised)STEMSimulationExperiment98%[64]
U-Net (supervised)STEMSimulationSimulation–[66]
U-Net (supervised)HRTEMExperimentExperiment93.17%[61]
MorphologyAutoDetect-mNP (unsupervised)TEMExperimentExperiment85%–95%[68]
FCN (regression&classification)STEMSimulationSimulation85%–93%[69]
CNNSTEMSimulationSimulation95%[60]
U-NetTEMExperimentExperiment–[63]
Genetic algorithmTEMExperimentExperiment99.75%[70]
StructureCNNFFTSimulationSimulation>97%[62]
CNNHRTEMSimulationSimulation90.5%[71]
SpectrumU-NetEELSExperimentExperiment>90%[65]
Neural network (unsupervised)EDXExperimentExperiment–[67]
DownLoad: CSV
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    Received: 12 February 2022 Revised: 15 March 2022 Online: Accepted Manuscript: 20 May 2022Uncorrected proof: 25 May 2022Published: 01 August 2022

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      Article Navigation >  Journal of Semiconductors  > 2022  >  43(8): 081001
      Zhiheng Cheng, Chaolun Wang, Xing Wu, Junhao Chu. Review in situ transmission electron microscope with machine learning[J]. Journal of Semiconductors, 2022, 43(8): 081001. doi: 10.1088/1674-4926/43/8/081001 Z H Cheng, C L Wang, X Wu, J H Chu. Review in situ transmission electron microscope with machine learning[J]. J. Semicond, 2022, 43(8): 081001. doi: 10.1088/1674-4926/43/8/081001Export: BibTex EndNote
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      Zhiheng Cheng, Chaolun Wang, Xing Wu, Junhao Chu. Review in situ transmission electron microscope with machine learning[J]. Journal of Semiconductors, 2022, 43(8): 081001. doi: 10.1088/1674-4926/43/8/081001

      Z H Cheng, C L Wang, X Wu, J H Chu. Review in situ transmission electron microscope with machine learning[J]. J. Semicond, 2022, 43(8): 081001. doi: 10.1088/1674-4926/43/8/081001
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      Review in situ transmission electron microscope with machine learning

      doi: 10.1088/1674-4926/43/8/081001

      • In Situ Devices Center, Shanghai Key Laboratory of Multidimensional Information Processing, East China Normal University, Shanghai 200241, China

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      • Author Bio:

        Zhiheng Cheng is currently an undergraduate student at East China Normal University (ECNU). He is majoring in electronic engineering. His research interest is in TEM data processing by machine learning

        Chaolun Wang received his PhD degree from University of Science and Technology Beijing. He then joined East China Normal University (ECNU) China as a postdoctoral scholar. Currently he is an associate professor at ECNU. His main research area is the synthesis and characterization of two-dimensional materials

        Xing Wu received her PhD degree from Nanyang Technological University Singapore. She then worked at the Singapore University of Design and Technology and Southeast University as a postdoctoral scholar. She is currently a professor at East China Normal University China. Her research interest is in situ TEM characterization of advanced electronic materials

        Junhao Chu received his PhD degrees from the Shanghai Institute of Technical Physics, Chinese Academy of Sciences (CAS) China. He is currently a professor at East China Normal University and the Shanghai Institute of Technical Physics of the CAS, and is a member of the CAS. His research interests include semiconductor physics and devices

      • Corresponding author: clwang@cee.ecnu.edu.cn; xwu@cee.ecnu.edu.cn
      • Received Date: 2022-02-12
      • Revised Date: 2022-03-15
        • Available Online: 2022-05-20

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