In-situ quantification of the surface roughness for facile fabrications of atomically smooth thin films

Liang, Genhao; Cheng, Ling; Zha, Junkun; Cao, Hui; Zhang, Jingxian; Liu, Qixin; Bao, Mingrui; Liu, Jia; Zhai, Xiaofang

doi:10.1007/s12274-021-3720-5

Cited by 9 publications

(5 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…After 62 min of deposition, the RHEED pattern of graphene completely disappeared, leaving only the ReSe 2 streaks, as shown in Fig. 2 g. The vertically elongated ReSe 2 streaks indicated a flat surface topography of the ReSe 2 thin film [ 9 ]. The in-plane lattice parameter of the ReSe 2 layer was estimated by comparing the RHEED streaks of graphene and ReSe 2 .…”

Section: Resultsmentioning

confidence: 99%

Machine-learning-assisted analysis of transition metal dichalcogenide thin-film growth

et al. 2023

View full text Add to dashboard Cite

In situ reflective high-energy electron diffraction (RHEED) is widely used to monitor the surface crystalline state during thin-film growth by molecular beam epitaxy (MBE) and pulsed laser deposition. With the recent development of machine learning (ML), ML-assisted analysis of RHEED videos aids in interpreting the complete RHEED data of oxide thin films. The quantitative analysis of RHEED data allows us to characterize and categorize the growth modes step by step, and extract hidden knowledge of the epitaxial film growth process. In this study, we employed the ML-assisted RHEED analysis method to investigate the growth of 2D thin films of transition metal dichalcogenides (ReSe2) on graphene substrates by MBE. Principal component analysis (PCA) and K-means clustering were used to separate statistically important patterns and visualize the trend of pattern evolution without any notable loss of information. Using the modified PCA, we could monitor the diffraction intensity of solely the ReSe2 layers by filtering out the substrate contribution. These findings demonstrate that ML analysis can be successfully employed to examine and understand the film-growth dynamics of 2D materials. Further, the ML-based method can pave the way for the development of advanced real-time monitoring and autonomous material synthesis techniques. Graphical Abstract

show abstract

Section: Resultsmentioning

confidence: 99%

Machine-learning-assisted analysis of transition metal dichalcogenide thin-film growth

et al. 2023

View full text Add to dashboard Cite

show abstract

“…It is noted that most RHEED patterns are not exactly streaky lines but contain spots, indicating the possible formation of islands in real space. 48 The atomic force microscopy (AFM) 5 μm × 5 μm images of corresponding samples are shown in Figure 4g− l, and the surface morphology of other samples was characterized by AFM as well (Figure S7, Supporting Information). The root-mean-square roughness of all samples, which is also listed in Table 1, agrees with the RHEED results.…”

Section: Resultsmentioning

confidence: 99%

“…These are also in good agreement with the results obtained by XRD. It is noted that most RHEED patterns are not exactly streaky lines but contain spots, indicating the possible formation of islands in real space . The atomic force microscopy (AFM) 5 μm × 5 μm images of corresponding samples are shown in Figure g–l, and the surface morphology of other samples was characterized by AFM as well (Figure S7, Supporting Information).…”

Section: Resultsmentioning

confidence: 99%

Investigation of Phase Transition and Ultrawide Band Gap Engineering in MgGaO Semiconductor Thin Films

Yang,

Shou,

Almujtabi

et al. 2023

ACS Appl. Opt. Mater.

View full text Add to dashboard Cite

Magnesium gallium oxide (MgGaO) ternary alloys with band gap energy larger than ∼5.0 eV can provide opportunities for optoelectronics in the deep ultraviolet spectral range and power electronics with extremely high critical field strength. It is important to grow high-quality MgGaO alloys with varied Mg compositions and understand their structural and optical properties. From this perspective, 20 MgGaO samples with Mg atomic percentages from 0 to 100% were grown by using oxygen plasmaassisted molecular beam epitaxy. Band gap tuning from 5.03 to 5.89 eV was achieved for the ternary alloys, and all samples had a transmittance of over ∼90% in the visible spectral range. The lattice structures were confirmed to transform from the β phase in Ga-rich materials to the β and rocksalt mixture phase in high-Ga high-Mg alloys and to the pure rocksalt phase in Mg-rich alloys. How lattice parameters change with the increase of Mg atom % and the epitaxy relationship between MgGaO films and c-sapphire substrates were revealed.

show abstract

“…RHEED is a powerful technique that monitors the surface state during lm growth because it provides extensive physical information, including surface morphology, growth rate, lattice spacing, crystalline disorder, and surface reconstruction. [74][75][76][77][78][79][80] Using the RHEED dataset, we will conduct a more quantitative analysis of the growth modes of 2D materials over time and identify crystal structures during the lm growth. [81][82][83][84] Along with the in situ RHEED data, we collected diverse surface analysis results for targeted samples, including AFM images, XPS, and PL/Raman spectra.…”

Section: Data Generationmentioning

confidence: 99%

https://2DMat.ChemDX.org: Experimental data platform for 2D materials from synthesis to physical properties

Yang,

Kang,

Kim

et al. 2024

Digital Discovery

View full text Add to dashboard Cite

show abstract

In-situ quantification of the surface roughness for facile fabrications of atomically smooth thin films

Cited by 9 publications

References 30 publications

Machine-learning-assisted analysis of transition metal dichalcogenide thin-film growth

Machine-learning-assisted analysis of transition metal dichalcogenide thin-film growth

Investigation of Phase Transition and Ultrawide Band Gap Engineering in MgGaO Semiconductor Thin Films

https://2DMat.ChemDX.org: Experimental data platform for 2D materials from synthesis to physical properties

Contact Info

Product

Resources

About