Characterization Of 2D Materials Research Articles

Fast adaptive focusing confocal Raman microscopy for large-area two-dimensional materials

Large-area two-dimensional (2D) materials possess significant potential in the development of next generation semiconductor due to their unique physicochemical properties. Confocal Raman spectroscopy (CRM), a typical 2D material characterization method, has a limited effective measurement area owing to the restricted focus depth of the system and the less-than-ideal level of the substrate. We propose fast adaptive focusing confocal Raman microscopy (FAFCRM) to realize real-time focusing detection for large-area 2D materials. By observing spot changes on the charge coupled device (CCD) caused by placing an aperture in front of the CCD, the methodology gives a focusing resolution up to 100 nm per 60 μm without axial scanning. A graphene was measured over 25.6 mm × 25.6 mm area on focus through all the scanning. The research results provide new perspectives for non-destructive characterization of 2D materials at the inch level.

Deterministic reflection contrast ellipsometry for thick multilayer two-dimensional heterostructures

Abstract Optical spectroscopy is a powerful tool for characterizing the properties of two-dimensional (2D) heterostructures. However, extracting the permittivity information of each 2D layer in optically thick heterostructures is challenging because of interference. To accurately measure the optical permittivity of each 2D layer in a heterostructure or on a substrate with a thick insulating spacer, such as oxides, we propose deterministic reflection contrast ellipsometry (DRCE). Our DRCE method has two advantages over conventional techniques. It deterministically measures the optical permittivity of 2D materials using only the measured reflection spectra of the heterostructure, rather than dispersion fitting as in spectroscopic ellipsometry. Additionally, the DRCE is free of excitonic energy errors in reflection-contrast spectroscopy. We believe that DRCE will enable accurate and rapid characterization of 2D materials.

Quantitative analysis of [formula omitted] thin film micrographs with machine learning

Isolating the features associated with different materials growth conditions is important to facilitate the tuning of these conditions for effective materials growth and characterization. This study presents machine learning models for classifying atomic force microscopy (AFM) images of thin film MoS2 based on their growth temperatures. By employing nine different algorithms and leveraging transfer learning through a pretrained ResNet model, we identify an effective approach for accurately discerning the characteristics related to growth temperature within the AFM micrographs. Robust models with test accuracies of up to 70% were obtained, with the best performing algorithm being an end-to-end ResNet fine-tuned on our image domain. Class activation maps and occlusion attribution reveal that crystal quality and domain boundaries play crucial roles in classification, with models exhibiting the ability to identify latent features that humans could potentially miss. Overall, the models demonstrated high accuracy in identifying thin films grown at different temperatures despite limited and imbalanced training data as well as variation in growth parameters besides temperature, showing that our models and training protocols are suitable for this and similar predictive tasks for accelerated 2D materials characterization.

(Invited, Digital Presentation) Integration of 2D Material Characterization and Reliability into Device Manufacturing

After the unique properties of graphene were realized, scientists began seeking other types of atomically thin materials that exhibited similar attractive and exciting properties. There is now a range of materials including single element (e.g. phosphorene) and compounds (e.g, transitional metal dichalcogenides and MXenes) that show promise for electronic, quantum, superconducting and optoelectronic applications. As with any new material, understanding structure and inherent defects is an important part to optimizing performance in these materials for their intended applications.Manufacturing optimized devices that incorporate newly-emerging materials requires predictable performance throughout device lifetimes. Unexpected degradation in device performance, sometimes leading to failure, is often traceable to poor material reliability. Reliability is rooted in the stability of the atoms making up a material, and is determined by geometry, microstructure, chemistry, dimensional scale, proximity to other materials, and exposure to external stressors. Integration of powerful characterization techniques and reliability tests of materials under operational conditions improves fundamental understanding and accelerates development of highly-reliable materials and devices.This talk will cover recent work on 2D materials characterization and reliability testing of these materials with the ultimate goal of manufacturability. Characterization of defects, primarily with transmission-SEM, to characterize diverse 2D materials (e.g., topological insulators, transition metal dichalcogenides, semiconductors) will be demonstrated. Initial measurement results to understand the impact of reliability challenges (e.g. electrical, thermal) the material may experience during manufacturing will be discussed. The advancement of this work to identify potential device configurations, material interfaces and use conditions that will be problematic will be presented.

Reinforced active learning for CVD-grown two-dimensional materials characterization

Two-dimensional (2D) materials are one of the research frontiers in material science due to their promising properties. Chemical Vapor Deposition (CVD) is the most widely used technique to grow large-scale high-quality 2D materials. The CVD-grown 2D materials can be efficiently characterized by an optical microscope. However, annotating microscopy images to distinguish the growth quality from good to bad is time-consuming. In this work, we explore Active Learning (AL), which iteratively acquires quality labels from a human and updates the classifier for microscopy images. As a result, AL only requires a limited amount of labels to achieve a good model performance. However, the existing handcrafted query strategies in AL are not good at dealing with the dynamics during the query process since the rigid handcrafted query strategies may not be able to choose the most informative instances (i.e., images) after each query. We propose a Reinforced Active Learning (RAL) framework that uses reinforcement learning to learn a query strategy for AL. Besides, by introducing the intrinsic motivation into the proposed framework, a unique intrinsic reward is designed to enhance the classification performance. The results show that RAL outperforms AL, and can significantly reduce the annotation efforts for the CVD-grown 2D materials characterization.

Mechanical properties characterization of 2D materials via pressure bulge testing

Pressure bulge testing is one of the standard techniques for characterizing the mechanical properties of thin films, whereas it is far less often used to characterize the mechanical properties of 2D materials. In the present work, the effectiveness of bulge testing on determining the mechanical properties of 2D materials is systematically investigated using numerical analysis. After assuming 2D materials as continuum thin films, we start by simulating the relationship between the pressure difference across the suspended film and the film deflection, and then fit the results with analytical models to extract the elastic modulus of the film. It should be noted that the clamped, pre-stretched film widely assumed in the existing analytical model is not appropriate for atomically thin materials; instead, freestanding 2D materials are actually slack in bulge testing due to the delamination of their adhesive boundary condition. Therefore, the initially unstressed flat films, pre-stretched films and slack films are investigated in the present work, and the Poisson’s ratio effect is considered to investigate the effectiveness of bulge testing for most 2D materials. It is found that bulge testing can be an effective approach to characterize the mechanical properties of 2D materials.

Effectiveness of the analytical models used in standard techniques for mechanical property characterization of 2D materials

Two dimensional (2D) materials are usually assumed to be continuum thin films to study their mechanical behavior. Freestanding indentation testing and pressure bulge testing are two standard techniques for characterizing the mechanical properties of thin films. Compared with the former, the latter is far less often used to characterize the elastic moduli of 2D materials, even though there is no solid evidence to show that the former is more effective than the latter. In the present work, the effectiveness of the analytical models adopted in both approaches on determining the elastic moduli of 2D materials are investigated using numerical analysis, which can be evaluated based upon the accuracy of the determined elastic moduli, and the effects of Poisson’s ratio and the applied loading range are also considered. To account for the effect of the adhesive boundary condition of freestanding 2D materials in the testing, the numerical simulations of the initially unstressed samples, pre-stretched samples and slack samples are all analyzed in the present work. It is found that the bulge testing model is actually more effective than the indentation model on determining the mechanical properties of 2D materials.

A universal substrate for the nanoscale investigation of two-dimensional materials

Since discovering two-dimensional materials, there has been a great interest in exploring, understanding, and taking advantage of their unique properties. Si/SiO2 is one of the most used substrates for the deposition and characterization of 2D materials due to its availability and optical contrast. This work goes beyond the conventional substrate and introduces highly-ordered pyrolytic graphite (HOPG) as universal support for investigating two-dimensional materials due to several unique properties such as chemical and temperature stability, intrinsic high flatness, reusability, electrical conductivity, ease of use, availability, and enhanced adhesion of two-dimensional materials. We demonstrate this by analyzing several 2D materials with advanced atomic force microscopy methods, Raman and photoluminescence spectroscopy with hyperspectral imaging, and scanning electron microscopy with elementary analysis imaging. The strong adhesion to HOPG allowed the instant deposition of different two-dimensional materials GaSe, MoS2, Zn2In2S5, talc, and h-BN. This feat is hard to accomplish on the conventional SiO2 substrate without polymer-assisted transfer. Moreover, this strong interaction can strain 2D materials deposited on HOPG, giving localized changes in reactivity, optical, and electronic properties. This effect is explored for selective Ag deposition on strained regions of 2D materials to activate photocatalytic reactions.

Field-effect conductivity scaling for two-dimensional materials with tunable impurity density

A scaling law is demonstrated in the conductivity of gated two-dimensional (2D) materials with tunable concentrations of ionized impurity scatterers. Experimental data is shown to collapse onto a single 2D conductivity scaling (2DCS) curve when the mobility is scaled by r, the relative impurity-induced scattering, and the gate voltage is shifted by , a consequence of impurity-induced doping. This 2DCS analysis is demonstrated first in an encapsulated 2D black phosphorus multilayer at with charge trap densities programmed by a gate bias upon cooldown, and next in a Bi2Se3 2D monolayer at room temperature exposed to varying concentrations of gas adsorbates. The observed scaling can be explained using a conductivity model with screened ionized impurity scatterers. The slope of the r vs. plot defines a disorder-charge specific scattering rate equivalent to a scattering strength per unit impurity charge density: indicates a preponderance of positively charged impurities with for negatively charged. This 2DCS analysis is expected to be applicable in arbitrary 2D materials systems with tunable impurity density, which will advance 2D materials characterization and improve performance of 2D sensors and transistors.

Methods for Measuring Thermal Conductivity of Two-Dimensional Materials: A Review.

Two-dimensional (2D) materials are widely used in microelectronic devices due to their excellent optical, electrical, and mechanical properties. The performance and reliability of microelectronic devices based 2D materials are affected by heat dissipation performance, which can be evaluated by studying the thermal conductivity of 2D materials. Currently, many theoretical and experimental methods have been developed to characterize the thermal conductivity of 2D materials. In this paper, firstly, typical theoretical methods, such as molecular dynamics, phonon Boltzmann transport equation, and atomic Green’s function method, are introduced and compared. Then, experimental methods, such as suspended micro-bridge, 3ω, time-domain thermal reflectance and Raman methods, are systematically and critically reviewed. In addition, the physical factors affecting the thermal conductivity of 2D materials are discussed. At last, future prospects for both theoretical and experimental thermal conductivity characterization of 2D materials is given. This paper provides an in-depth understanding of the existing thermal conductivity measurement methods of 2D materials, which has guiding significance for the application of 2D materials in micro/nanodevices.

Electronically Tunable Transparent Conductive Thin Films for Scalable Integration of 2D Materials with Passive 2D–3D Interfaces

AbstractA novel transparent conductive support structure for scalable integration of 2D materials is demonstrated, providing an electronically passive 2D–3D interface while also enabling facile interfacial charge transport. This structure, which comprises an evaporated nanocrystalline carbon (nc‐C) film beneath nanometer‐thin atomic layer deposited AlOx, is thermally stable and allows direct chemical vapor deposition of 2D materials onto the surface. The combination of spatial uniformity, enhanced charge screening, and low interface defect concentrations yields a tenfold enhancement of MoS2 photoluminescence intensity compared to flakes on conventional Si/SiO2, while also retaining the strong optical contrast for monolayer flakes. Tunneling across the ultrathin AlOx enables facile interfacial charge injection, which is utilized for high‐resolution scanning electron microscopy and photoemission electron microscopy with no detectable charging. Thus, this combination of scalable fabrication and electronic conductivity across a weakly interacting 2D–3D interface opens up new opportunities for device integration and characterization of 2D materials.

Effectiveness of Standard Techniques for Mechanical Property Characterization of 2d Materials

Effectiveness of Standard Techniques for Mechanical Property Characterization of 2d Materials

(Invited) Atomic-Scale Functionalization and Characterization of 2D Materials

As 2D materials set their path into our next generation nanoelectronics, enriching their functionalities and complementing their limitations become more important. In particular, novel strategies for controllable incorporation of substitutional and intercalated atoms need to be developed. Here, we report on modifying 2D materials at the atomic-scale by ultra-low energy ion implantation to functionalize their properties and explore their limits. I will present our recent work on graphene on two topics: substitutional manganese (Mn) atoms for magnetic doping [1] and intercalation of noble gases to produce nanobubbles with small radii and high aspect ratios. On the one hand, we have characterized in detail the atomic structure of substitutional Mn and graphene’s Dirac-like band structure doped with up to 1% substitutional Mn. On the other hand, the structural inspection of our graphene nanobubbles has revealed a so far unexplored small-radius limit, associated with high aspect ratios as well as extreme pressure and strain. Our approach is based on a wide range of characterization techniques (structural and electronic), including scanning tunneling microscopy and spectroscopy (STM/STS), Raman spectroscopy, synchrotron-based X-ray photoelectron spectroscopy (XPS), angle-resolved photoemission spectroscopy (ARPES), X-ray magnetic circular dichroism (XMCD), among others. These experimental studies are complemented by density functional theory (DFT) and molecular dynamics (MD) simulations.[1] P. C. Lin et al., ACS nano 15(3), 5449-5458 (2021). Figure 1

Atomic-level defect modulation and characterization methods in 2D materials

Two-dimensional (2D) materials are attracting increasing research interest owing to their distinct tunable physical properties. Moreover, the ubiquitous defects in 2D materials offer an opportunity to tailor their electronic properties. Recently, atomic-level structural modification methods for 2D materials have been developed, further triggering the need for the precise control of defects. Following the ground-breaking advancements in the atomic-scale characterization of defects in 2D materials, valuable information on defect-driven electronic properties has been published. It is therefore important to present a review work on the recent research developments on atomic-level defect control and characterization of 2D materials. This Perspective highlights the type and role of atomic defects in 2D materials, as well as some current technologies for engineering such defects. In addition, we emphasize on atomic-level characterization methods with a focus on aberration-corrected transmission electron microscopy and deep learning as a powerful method for characterizing defects in 2D materials. Based on the two characterization techniques, we present the experimental results of laser-induced structurally modified MoTe2 and transition metal decorated h-BN. We believe that this work will provide fundamental knowledge for engineering and characterizing defects in 2D materials for the design of application-specific electronic devices.

Structural defects in transition metal dichalcogenide core-shell architectures

Curvature presents a powerful approach to design atomic structure and tailor material properties in atomically thin transition metal dichalcogenides (TMDs). The emerging TMD core-shell architecture, in which a multilayer TMD shell encapsulates a curved nanoparticle core, presents the opportunity to controllably induce defects into a TMD crystal by strategically constructing the shape of the underlying core. However, harnessing this potential platform first requires robust characterization of the unique structural features present in the core-shell architecture. To this end, transmission electron microscopy (TEM) and scanning TEM (STEM) are particularly powerful tools for direct structural characterization of 2D materials with a high spatial resolution and precision. Here, we reveal and describe defects inherently present in the TMD core-shell architecture. We develop a comprehensive framework to classify the observed defects and discuss potential origins and implications of structural variations. We utilize high resolution S/TEM to reveal the relationship between defects and their associated strain fields. Furthermore, we demonstrate that TMD shells often possess a wide range of interlayer spacings with varied spatial distribution. By exploring the rich array of structural defects inherently present in the TMD core-shell architecture, we provide an important foundation to ultimately induce exotic properties in TMDs through sophisticated defect engineering.

In Situ and Operando Characterizations of 2D Materials in Electrochemical Energy Storage Devices

In-Situ/Operando Characterization of 2D Materials The recent advances of in-situ and operando characterization of 2D materials for electrochemical energy storage devices are reviewed in the article number 2000076 by Qiang Fu, Klaus Müllen, Zhong-Shuai Wu, and co-workers to comprehensively understand the charge storage and degradation mechanisms. Preparation of 2D materials and the impact of preparation methods on the resulting electrode structure and electrochemical performance, and the mechanisms underlying the operation of supercapacitors and lithium batteries are discussed.

MoS2/h-BN/Graphene Heterostructure and Plasmonic Effect for Self-Powering Photodetector: A Review.

A photodetector converts optical signals to detectable electrical signals. Lately, self-powered photodetectors have been widely studied because of their advantages in device miniaturization and low power consumption, which make them preferable in various applications, especially those related to green technology and flexible electronics. Since self-powered photodetectors do not have an external power supply at zero bias, it is important to ensure that the built-in potential in the device produces a sufficiently thick depletion region that efficiently sweeps the carriers across the junction, resulting in detectable electrical signals even at very low-optical power signals. Therefore, two-dimensional (2D) materials are explored as an alternative to silicon-based active regions in the photodetector. In addition, plasmonic effects coupled with self-powered photodetectors will further enhance light absorption and scattering, which contribute to the improvement of the device’s photocurrent generation. Hence, this review focuses on the employment of 2D materials such as graphene and molybdenum disulfide (MoS2) with the insertion of hexagonal boron nitride (h-BN) and plasmonic nanoparticles. All these approaches have shown performance improvement of photodetectors for self-powering applications. A comprehensive analysis encompassing 2D material characterization, theoretical and numerical modelling, device physics, fabrication and characterization of photodetectors with graphene/MoS2 and graphene/h-BN/MoS2 heterostructures with plasmonic effect is presented with potential leads to new research opportunities.

Growth and in situ characterization of 2D materials by chemical vapour deposition on liquid metal catalysts: a review.

2D materials (2DMs) have now been established as unique and attractive alternatives to replace current technological materials in a number of applications. Chemical vapour deposition (CVD), is undoubtedly the most renowned technique for thin film synthesis and meets all requirements for automated large-scale production of 2DMs. Currently most CVD methods employ solid metal catalysts (SMCat) for the growth of 2DMs however their use has been found to induce structural defects such as wrinkles, fissures, and grain boundaries among others. On the other hand, liquid metal catalysts (LMCat), constitute a possible alternative for the production of defect-free 2DMs albeit with a small temperature penalty. This review is a comprehensive report of past attempts to employ LMCat for the production of 2DMs with emphasis on graphene growth. Special attention is paid to the underlying mechanisms that govern crystal growth and/or grain consolidation and film coverage. Finally, the advent of online metrology which is particularly effective for monitoring the chemical processes under LMCat conditions is also reviewed and certain directions for future development are drawn.

Application of Second Harmonic Generation in Characterization of 2D Materials

Application of Second Harmonic Generation in Characterization of 2D Materials

High‐Frequency Contactless Characterization of 2D Materials. Graphene, WS2

The electrical conductivity of two‐dimensional (2D) materials without any electrical contact can be obtained using two different methods: the terahertz time domain spectroscopy (THz‐TDS) method, in the range from GHz up to 2 THz, and with a rutile dielectric resonator (RDR), in which case the conductivity is obtained at the resonant frequency of the device, close to 9.0 GHz. In one case (THz‐TDS in a transmission setup), the sample is directly focused. In the other case (RDR), the sample is placed inside the resonant cavity working at mode and must have exactly the same surface size as the cavity, 12 × 12 mm in our device. From the Q factor variation of the resonant cavity due to the sample, its surface resistance is extracted. These measurements are performed on different 2D materials: graphene and . Both methods are analyzed and compared. For few‐layer 2D samples, the THz‐TDS method is suitable.