No Arabic abstract
Indium selenide (InSe), as a novel van der Waals layered semiconductor, has attracted a large research interest thanks to its excellent optical and electrical properties in the ultra-thin limit. Here, we discuss four different optical methods to quantitatively identify the thickness of thin InSe flakes on various substrates, such as SiO2/Si or transparent polymeric substrates. In the case of thin InSe deposited on a transparent substrate, the transmittance of the flake in the blue region of the visible spectrum can be used to estimate the thickness. For InSe supported by SiO2/Si, the thickness of the flakes can be estimated either by assessing their apparent colors or accurately analyzed using a Fresnel-law based fitting model of the optical contrast spectra. Finally, we also studied the thickness dependency of the InSe photoluminescence emission energy, which provides an additional tool to estimate the InSe thickness and it works both for InSe deposited on SiO2/Si and on a transparent polymeric substrate.
Two-dimensional (2D) materials and their heterostructures, with wafer-scale synthesis methods and fascinating properties, have attracted numerous interest and triggered revolutions of corresponding device applications. However, facile methods to realize accurate, intelligent and large-area characterizations of these 2D structures are still highly desired. Here, we report a successful application of machine-learning strategy in the optical identification of 2D structure. The machine-learning optical identification method (MOI method) endows optical microscopy with intelligent insight into the characteristic colour information in the optical photograph. Experimental results indicate that the MOI method enables accurate, intelligent and large-area characterizations of graphene, molybdenum disulphide (MoS2) and their heterostructures, including identifications of the thickness, the existence of impurities, and even the stacking order. Thanks to the convergence of artificial intelligence and nanoscience, this intelligent identification method can certainly promote the fundamental research and wafer-scale device application of 2D structures.
Considering that two-dimensional (2D) molybdenum trioxide has acquired more attention in the last few years, it is relevant to speed up thickness identification of this material. We provide two fast and non-destructive methods to evaluate the thickness of MoO3 flakes on SiO2/Si substrates. First, by means of quantitative analysis of the apparent color of the flakes in optical microscopy images, one can make a first approximation of the thickness with an uncertainty of +-3 nm. The second method is based on the fit of optical contrast spectra, acquired with micro-reflectance measurements, to a Fresnel law-based model that provides an accurate measurement of the flake thickness with +-2 nm of uncertainty.
We have implemented three different optical methods to quantitatively assess the thickness of thin GaSe flakes transferred on both transparent substrates, like Gel-Film, or SiO2/Si substrates. We show how their apparent color can be an efficient way to make a quick rough estimation of the thickness of the flakes. This method is more effective for SiO2/Si substrates as the thickness dependent color change is more pronounced on these substrates than on transparent substrates. On the other hand, for transparent substrates, the transmittance of the flakes in the blue region of the visible spectrum can be used to estimate the thickness. We find that the transmittance of flakes in the blue part of the spectrum decreases at a rate of 1.2%/nm. On SiO2/Si, the thickness of the flakes can be accurately determined by fitting optical contrast spectra to a Fresnel law-based model. Finally, we also show how the quantitative analysis of transmission mode optical microscopy images can be a powerful method to quickly probe the environmental degradation of GaSe flakes exposed to aging conditions.
Recent advances in high-throughput experimentation for combinatorial studies have accelerated the discovery and analysis of materials across a wide range of compositions and synthesis conditions. However, many of the more powerful characterization methods are limited by speed, cost, availability, and/or resolution. To make efficient use of these methods, there is value in developing approaches for identifying critical compositions and conditions to be used as a-priori knowledge for follow-up characterization with high-precision techniques, such as micron-scale synchrotron based X-ray diffraction (XRD). Here we demonstrate the use of optical microscopy and reflectance spectroscopy to identify likely phase-change boundaries in thin film libraries. These methods are used to delineate possible metastable phase boundaries following lateral-gradient Laser Spike Annealing (lg-LSA) of oxide materials. The set of boundaries are then compared with definitive determinations of structural transformations obtained using high-resolution XRD. We demonstrate that the optical methods detect more than 95% of the structural transformations in a composition-gradient La-Mn-O library and a Ga$_2$O$_3$ sample, both subject to an extensive set of lg-LSA anneals. Our results provide quantitative support for the value of optically-detected transformations as a priori data to guide subsequent structural characterization, ultimately accelerating and enhancing the efficient implementation of $mu$m-resolution XRD experiments.
Nanocrystalline n-AlN:Er thin films were deposited on (001) Silicon substrates by r. f. magnetron sputtering at room temperature to study the correlation between 1.54 $mu$m IR photoluminescence (PL) intensity, AlN crystalline structure and Er concentration rate. This study first presents how Energy-Dispersive Spectroscopy of X-rays (EDSX) Er Cliff Lorimer sensitivity factor alpha = 5 is obtained by combining EDSX and electron probe micro analysis (EPMA) results on reference samples. It secondly presents the relative PL intensities of nanocrystallized samples prepared with identical sputtering parameters as a function of the Er concentration. The structure of crystallites in AlN films is observed by transmission electron microscopy.