Tailoring atomic 1T phase CrTe₂ for in situ fabrication

Abstract

Controllable tailoring and understanding the phase-structure relationship of the 1T phase two-dimensional (2D) materials are critical for their applications in nanodevices. The in situ transmission electron microscope (TEM) could regulate and monitor the evolution process of the nanostructure of 2D material with atomic resolution. In this work, a controllably tailoring 1T-CrTe2 nanopore is carried out by the in situ TEM. A preferred formation of the 1T-CrTe2 border structure and nanopore healing process are studied at the atomic scale. The controllable tailoring of the 1T phase nanopore could be achieved by regulating the transformation of two types of low indices of crystal faces { 10 1 ¯ 0 } and { 11 2 ¯ 0 } at the nanopore border. Machine learning is applied to automatically process the TEM images with high efficiency. By adopting the deep-learning-based image segmentation method and augmenting the TEM images specifically, the nanopore of the TEM image could be automatically identified and the evaluation result of DICE metric reaches 93.17% on test set. This work presents the unique structure evolution of 1T phase 2D material and the computer aided high efficiency TEM data analysis based on deep learning. The techniques applied in this work could be generalized to other materials for controlled nanostructure regulation and automatic TEM image analyzation.

Applications and potentials of machine learning in optoelectronic materials research: An overview and perspectives

“…Chen et al [129] developed a CNN model to establish correlations between the UV-vis absorption spectra and chemical compositions of gold nanoclusters, enabling the identification of nanocluster compositions from featureless absorption spectra. Wang et al [130] demonstrated the feasibility of CNN-based TEM image analysis for the structural control of 1T-phase CrTe 2 . The approaches in this study could be extended to the structural control of other 1T or 2H phase 2D materials, providing vital support for emerging 2D material optoelectronic devices.…”

Deep learning analysis on transmission electron microscope imaging of atomic defects in two-dimensional materials

“…present the border structure evolution of 1T phase 2D material ( Figure 7 A). 53 Using the deep learning method, the edge morphology evolutions are automatically identified. The crystal faces of the edge can also be analyzed.…”

“…Combining the advantages of TEM and deep learning, many interesting and complex works, including accurate identification, quantitative analysis, prediction, and mechanism exploration, can be completed automatically ( Figure 2 ). 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 In this review, the recent developments in the TEM data analysis of defects in 2D materials using deep learning technology are summarized. We first present the technical evolution of the TEM data analysis.…”

Review in situ transmission electron microscope with machine learning

“…As an alternative to labeled experimental data, the simulated TEM data with accurate ground truth is easier to obtain. The multislice calculation-based algorithm is usually applied for TEM data simulation, including STEM images [60,64] Using the FCN algorithm, the atom positions and types in a lattice are recognized with high accuracy, enabling effective defect determination. The presented algorithm can be extended to other automatic TEM image analysis that relates to the atomic position.…”

“…Second, in Zhang et al the convergent beam electron diffraction (CBED), namely 4D STEM, is adopted to assist the CNN-based thickness detection [60] . As is shown in Fig.…”