Chemical vapor deposition of 2D materials: A review of modeling, simulation, and machine learning studies

Bhowmik, Sayan; Rajan, Ananth Govind

doi:10.1016/j.isci.2022.103832

Cited by 52 publications

(27 citation statements)

References 172 publications

(273 reference statements)

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“…As briefly anticipated in the introduction, ML can be a powerful tool for material science and design, owing to its ability to efficiently find patterns and trends, handle multidimensional and heterogeneous data and improve continuously with access to more observations-all while being intrinsically and fully automatable. Models and simulations based on ML approaches can, for instance, quickly narrow down the parameter space of specific variables involved in fabrication processes [18,181,182]. They can also be used to control, tweak or even design ad hoc properties of materials [131], heterostructures [9,132] and devices [19, 136,137], again while being suitable for fabrication strategies that require large-scale, fast and automated production.…”

Section: Color Center Synthesis and Stabilitymentioning

confidence: 99%

Machine and quantum learning for diamond-based quantum applications

Stone

Bradac²

2023

Mater. Quantum. Technol.

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In recent years, machine and quantum learning have gained considerable momentum sustained by growth in computational power and data availability and have shown exceptional aptness for solving recognition- and classification-type problems, as well as problems that require complex, strategic planning. In this work, we discuss and analyze the role machine and quantum learning are playing in the development of diamond-based quantum technologies. This matters as diamond and its optically-addressable spin defects are becoming prime hardware candidates for solid state-based applications in quantum information, computing and metrology. Through a selected number of demonstrations, we show that machine and quantum learning are leading to both practical and fundamental improvements in measurement speed and accuracy. This is crucial for quantum applications, especially for those where coherence time and signal-to-noise ratio are scarce resources. We summarize some of the most prominent machine and quantum learning approaches that have been conducive to the presented advances and discuss their potential for proposed and future quantum applications.

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Section: Color Center Synthesis and Stabilitymentioning

confidence: 99%

Machine and quantum learning for diamond-based quantum applications

Stone

Bradac²

2023

Mater. Quantum. Technol.

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“…In thin film processing, machine-learning methods have been successfully employed to improve materials growth, lithography, and etching steps. For materials growth, use of the data sciences to improve the quality of grown materials has become a field in itself . To cite a representative example, Bayesian optimization can be used to identify material growth parameters in just a few trials that yield optimal films with virtually no porosity (Figure a).…”

Section: Fabrication Of Freeform Devicesmentioning

confidence: 99%

Algorithm-Driven Paradigms for Freeform Optical Engineering

2022

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Advances in modern manufacturing have enabled the multiscalar patterning of dielectric media with nearly arbitrary layouts, presenting unique opportunities to revolutionize the design and fabrication pipeline for photonic technologies. In this Perspective, we discuss how algorithms based on classical optimization and deep learning are establishing a new conceptual framework for freeform optical engineering. These tools can specify suitable design parameters for a desired objective, automate the high-speed optimization of freeform devices, and augment manufacturing processes to mitigate challenges set by freeform fabrication. A central feature of many of these algorithms is their utilization of data and physics to model and exploit high-dimensional relationships between geometric structure and electromagnetic response within the constraints of Maxwell's equations. We anticipate that these algorithm-driven methods will streamline optical systems design at the physical limits of structured media and become standard academic and industry tools for scientists and engineers.

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“…As a typical bottom-up synthesis method, CVD presents prominent advantages among the various preparation technologies for 2D materials. − In addition, the well-developed derivative technologies of CVD (plasma-enhanced CVD, metal organic CVD, etc.) provide great opportunities to explore novel 2D species .…”

Section: Introductionmentioning

confidence: 99%

Controllable Synthesis of Novel Two-Dimensional Nonlayered β-Bi₂Te₄O₁₁ Flakes for Promising Optoelectronic Applications

et al. 2022

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Exploration of two-dimensional (2D) nonlayered materials is a significant work for developing the 2D materials family, owing to their intriguing physicochemical properties and promising applications in various fields. Herein, we present the controllable chemical vapor deposition (CVD) synthesis of nonlayered β-Bi2Te4O11 with a 2D morphology. It was revealed that the 2D growth of β-Bi2Te4O11 depends on the proper precursor. Moreover, the corresponding mechanism was demonstrated by density functional theory calculations. Moreover, the growth conditions of 2D β-Bi2Te4O11 were optimized by a machine learning (ML) strategy. The resultant 2D β-Bi2Te4O11 flakes with a large domain size (∼120 μm) and ultrathin thickness (∼9.5 nm) indicate promising applications of ML in guiding the CVD synthesis of 2D nonlayered materials. Compared with the bulk β-Bi2Te4O11 with an antiglass structure, the as-grown 2D β-Bi2Te4O11 shows a unique cation-ordering superstructure. In addition, the 2D β-Bi2Te4O11-based photodetector shows a prominent responsivity of 79.5 A W–1 at 375 nm. This work shows an efficient strategy for preparing 2D nonlayered materials and provides a brand-new platform to study the properties and applications of 2D nonlayered materials.

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Chemical vapor deposition of 2D materials: A review of modeling, simulation, and machine learning studies

Cited by 52 publications

References 172 publications

Machine and quantum learning for diamond-based quantum applications

Machine and quantum learning for diamond-based quantum applications

Algorithm-Driven Paradigms for Freeform Optical Engineering

Controllable Synthesis of Novel Two-Dimensional Nonlayered β-Bi₂Te₄O₁₁ Flakes for Promising Optoelectronic Applications

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Chemical vapor deposition of 2D materials: A review of modeling, simulation, and machine learning studies

Cited by 52 publications

References 172 publications

Machine and quantum learning for diamond-based quantum applications

Machine and quantum learning for diamond-based quantum applications

Algorithm-Driven Paradigms for Freeform Optical Engineering

Controllable Synthesis of Novel Two-Dimensional Nonlayered β-Bi2Te4O11 Flakes for Promising Optoelectronic Applications

Contact Info

Product

Resources

About

Controllable Synthesis of Novel Two-Dimensional Nonlayered β-Bi₂Te₄O₁₁ Flakes for Promising Optoelectronic Applications