High-Throughput Ellipsometric Contrast Microscopy of Lateral 2D Heterostructures for Optoelectronics.

IF 10.7 2区 材料科学 Q1 CHEMISTRY, PHYSICAL
Teja Potočnik, Oliver Burton, Suman K Chakraborty, Purbasha Ray, Ralf Mouthaan, Peter J Christopher, Zeinab Tirandaz, Xiaofan Lin, Hannah J Joyce, Stephan Hofmann, Prasana K Sahoo, Jack A Alexander-Webber
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引用次数: 0

Abstract

Covalently-bonded lateral 2D heterostructures offer unique (opto)electronic functionalities and can be deposited during a single growth process. However, the position of lateral junctions is typically uncontrolled due to random nucleation processes, which necessitates post-growth identification of suitable heterojunction regions for device integration. Here, ellipsometric contrast microscopy (ECM) is demonstrated to evaluate 2D lateral monolayer MoSe2-WSe2 and MoS2-WS2 heterostructures, which enables rapid imaging with high material-contrast down to sub-nanometer thickness for high-throughput characterisation of heterostructure domains. In addition, a computer vision algorithm provides precise identification of individual monolayer heterostructure junctions and their integration into rectifying devices and photodetectors. These results establish the advantages of ECM for reliable, fast characterization and large-scale integration of atomically thin 2D heterostructures into advanced optoelectronic devices, with potential extension to other nanomaterials.

用于光电子学的横向二维异质结构的高通量椭圆偏振对比显微镜。
共价键的横向二维异质结构提供了独特的(光电)电子功能,并且可以在单一生长过程中沉积。然而,由于随机成核过程,侧结的位置通常是不受控制的,这就需要在生长后识别适合器件集成的异质结区域。在这里,椭圆偏振对比显微镜(ECM)被证明可以评估二维横向单层MoSe2-WSe2和MoS2-WS2异质结构,它可以快速成像,具有高材料对比度,低至亚纳米厚度,用于异质结构域的高通量表征。此外,计算机视觉算法提供了单个单层异质结构结的精确识别,并将其集成到整流器件和光电探测器中。这些结果确立了ECM在可靠、快速表征和大规模集成原子薄二维异质结构到先进光电器件中的优势,并具有扩展到其他纳米材料的潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Small Methods
Small Methods Materials Science-General Materials Science
CiteScore
17.40
自引率
1.60%
发文量
347
期刊介绍: Small Methods is a multidisciplinary journal that publishes groundbreaking research on methods relevant to nano- and microscale research. It welcomes contributions from the fields of materials science, biomedical science, chemistry, and physics, showcasing the latest advancements in experimental techniques. With a notable 2022 Impact Factor of 12.4 (Journal Citation Reports, Clarivate Analytics, 2023), Small Methods is recognized for its significant impact on the scientific community. The online ISSN for Small Methods is 2366-9608.
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