Spectroscopic imaging ellipsometry for automated search of flakes of mono- and n-layers of 2D-materials

Funke, Sebastian; Wurstbauer, Ursula; Miller, Bastian; Matković, Aleksandar; Green, A.; Diebold, Alain C.; Röling, Christian; Thiesen, Peter

doi:10.1016/j.apsusc.2016.10.158

Cited by 33 publications

(47 citation statements)

References 17 publications

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“…3, where different spectral behaviors have been identified by a "flake search" routine which is described in greater detail in Ref. [13]. In the flake search, every pixel of the spectroscopic ψ and ∆ maps is compared to reference values at a given point in the spectra.…”

Section: Spectroscopic Resultsmentioning

confidence: 99%

Spectroscopic imaging ellipsometry of self-assembled SiGe/Si nanostructures

Alonso

Funke²,

González³

et al. 2017

Applied Surface Science

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Section: Spectroscopic Resultsmentioning

confidence: 99%

Spectroscopic imaging ellipsometry of self-assembled SiGe/Si nanostructures

Alonso

Funke²,

González³

et al. 2017

Applied Surface Science

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“…The refractive index and extinction coefficient were extracted and compared, with absorption peaks and layer‐number dependence observed and analyzed. Spectroscopic imaging ellipsometry working in the range of 250–1700 nm was used for the imaging of graphene on SiO 2 /Si substrate, covering an area about 1.5 mm × 1.2 mm . Both simulation and experiments were conducted, with graphene flakes found and monolayer search results shown in Figure .…”

Section: Microscale Spectral Mapping and Optical‐property Studiesmentioning

confidence: 99%

Section: Microscale Spectral Mapping and Optical‐property Studiesmentioning

confidence: 99%

Microscale Spectroscopic Mapping of 2D Optical Materials

Dong

Yetisen

Köhler

et al. 2019

Advanced Optical Materials

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be used for flexible touch screens and displays, printable electronics, and thinfilm photovoltaics, [4] while FETs made of graphene can be used for highly sensitive biosensors. [5] Another 2D material MoS 2 , a direct-bandgap semiconductor, has applications in ultrasensitive photodetectors, transistors, and gas-sensing devices. [6][7][8][9] Other 2D materials have also shown potential in photovoltaic devices, memory, and light-emitting diodes (LEDs). [10,11] Broad application potential of 2D materials in optoelectronic devices, photonic devices, and optical sensors is mainly based on their unique optical performances. [12] 2D materials can be fabricated based on their layered structure. In the molecular structures of these materials, the atoms are bonded hard in the same plane, but the bond effect between two lateral layers is weak due to van der Waals forces. [13] 2D materials can be roughly categorized as semimetal like graphene, semiconductor like transition metal dichalcogenides (TMDs), and insulator like hexagonal boron nitride (hBN) from the aspect of bandgap differences. [14] Due to the experimentally practical manipulation of monolayer and multilayer 2D materials, remarkable optical performances can be realized for a wide range of optical applications. For example, the number of layers of 2D materials can strongly influence the photoluminescence performance. [15] The unconventional optical performances including adsorption and emission, light sensitivity, as well as plasmonic effects of 2D materials are closely related to their inner physical properties such as carrier mobility, density of states, and band structure based on the interaction of atoms, structural symmetry, and stacking patterns. [16,17] Through the control of layer number which could influence the bandgap value, 2D materials could react to different spectrum ranging from ultraviolet to infrared light. [18] Doping strategies are also effective to modify intrinsic 2D semiconductors and improve the response with respect to an extended range of spectrum. [19,20] The assessment of the optical properties of 2D materials is indispensable for device and sensor development. [21] Based on detailed surface mapping and spectral information, the suitability of 2D materials for optical devices or sensor applications can be thoroughly studied. [22,23] Microscopy, direct 2D mapping of materials, can provide basic spatial information, [24] while spectroscopy can offer one more dimensional spectral information which is 2D materials have unique optical properties due to their ultrathin layered structures, and are emerging as promising materials for optoelectronic devices. To characterize and evaluate these properties, diffraction-limited spectroscopic mapping, also known as microscale spectroscopic imaging, has been developed to provide abundant spatial and spectral information. The usefulness of microscale spectroscopic mapping for unique property study of 2D optical materials is addressed. Advances in both mature and growing microscale spectroscopic mapping...

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“…However, the observed difference in contrast and color of a flake with respect to the background not only depends on its thickness and material but also on the substrate that is used and on the settings of the microscope. This large parameter space makes the identification of usable flakes tedious and, while there exist proposed algorithmic solutions [14][15][16][17][18][19][20][21], a sufficiently general and fast algorithm is difficult to formulate. So far, many existing algorithmic approaches have concentrated on rule-based image processing [16] and a combination of the latter with machine learning [15,17].…”

Section: Introductionmentioning

confidence: 99%

“…This large parameter space makes the identification of usable flakes tedious and, while there exist proposed algorithmic solutions [14][15][16][17][18][19][20][21], a sufficiently general and fast algorithm is difficult to formulate. So far, many existing algorithmic approaches have concentrated on rule-based image processing [16] and a combination of the latter with machine learning [15,17]. While these methods are successful in the specific conditions of the respective study, it may be hard to generalize them to different experimental * geliska@phys.ethz.ch conditions, such as, e.g., various camera settings, illumination conditions, or substrates.…”

Section: Introductionmentioning

confidence: 99%

Fully Automated Identification of Two-Dimensional Material Samples

et al. 2020

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Thin nanomaterials are key constituents of modern quantum technologies and materials research. The identification of specimens of these materials with the properties required for the development of state-of-the-art quantum devices is usually a complex and tedious human task. In this work, we provide a neural-network-driven solution that allows for accurate and efficient scanning, data processing, and sample identification of experimentally relevant two-dimensional materials. We show how to approach the classification of imperfect and imbalanced data sets using an iterative application of multiple noisy neural networks. We embed the trained classifier into a comprehensive solution for end-to-end automatized data processing and sample identification.

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Spectroscopic imaging ellipsometry for automated search of flakes of mono- and n-layers of 2D-materials

Cited by 33 publications

References 17 publications

Spectroscopic imaging ellipsometry of self-assembled SiGe/Si nanostructures

Spectroscopic imaging ellipsometry of self-assembled SiGe/Si nanostructures

Microscale Spectroscopic Mapping of 2D Optical Materials

Fully Automated Identification of Two-Dimensional Material Samples

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