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Fingerprinting the impact of p- and n-type dopants on the structural and local electrical properties of Indium Selenide by means of state-of-the-art transmission electron microscopy

14 Sept 2021, 12:30
20m
Room 3

Room 3

Oral Presentation A6. Characterisation of functional materials A6_Characterisation of functional materials

Speaker

Mr Abel Brokkelkamp (Delft University of Technology)

Description

Indium Selenide (InSe) is a remarkable two-dimensional quantum material whose characteristic properties include a bandgap that increases with fewer layers and a high controllability of p- and n-type doping. Furthermore, its bandgap can lie in the near infrared region, depending on the specific crystalline phase adopted. Indium Selenide is known to crystallize in either the β(2H)-, γ(3R)- or the ε(2H)-phase. Of these three crystalline phases, only the β(2H) and γ(3R) ones exhibit a direct bandgap, which makes them particularly suitable for optoelectronic applications. However, while the β(2H)-phase can be easily disentangled from the other, the γ(3R)- and ε(2H)-phases are indistinguishable from the in-plane point of view and appear also very similar from the out-of-plane one. An attractive possibility to tailor the optoelectronic properties of InSe is based on the introduction of dopants, which leads to structural defects and can modify the crystalline structure and symmetry properties of the material. In this work, we investigate p- and n-doped (as well as undoped) Indium Selenide by means of Transmission Electron Microscopy and related techniques. We determine the crystalline phase present in these Indium Selenide specimens with High Resolution TEM by means of a systematic investigation of both in- and out-of-plane cross-sections, and compare the structural differences arising from different types and amounts of doping. We further assess the impact of dopants on the local electronic properties using Electron Energy-Loss Spectroscopy (EELS) by comparing how relevant features in the spectra (including surface- and edge-related properties) depend on the doping. Finally, we deploy Machine Learning techniques for a model-independent subtraction of the Zero Loss Peak, which makes possible identifying in an unbiased manner the impact of dopants in InSe in the ultra-low-loss region of the EELS spectra.

Speaker Country Netherlands

Author

Mr Abel Brokkelkamp (Delft University of Technology)

Co-authors

Prof. Albert Davydov (National Institute of Standards and Technology) Prof. Juan Rojo (Nikhef Theory Group; VU Amsterdam) Ms Laurien Roest (Delft University of Technology) Ms Postmes Isabel (Delft University of Technology) Dr Sergiy Krylyuk (National Institute of Standards and Technology) Prof. Sonia Conesa-Boj (Delft University of Technology) Mr ter Hoeve Jaco (Nikhef Theory Group; VU Amsterdam)

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