RT journal article
T1 STEM Tools for Semiconductor Characterization: Beyond High-Resolution Imaging
A1 Mata Fernández, María de la
A1 Molina Rubio, Sergio Ignacio
A2 Ciencia de los Materiales e Ingeniería Metalúrgica y Química Inorgánica
K1 STEM atomic-resolution
K1 spectroscopy
K1 VEELS; 4D-STEM
K1 electric field mapping
K1 in situ
K1 opto-electronic properties
K1 semiconductor nanostructures
AB The smart engineering of novel semiconductor devices relies on the development of optimized functional materials suitable for the design of improved systems with advanced capabilities aside from better efficiencies. Thereby, the characterization of these materials at the highest level attainable is crucial for leading a proper understanding of their working principle. Due to the striking effect of atomic features on the behavior of semiconductor quantum- and nanostructures, scanning transmission electron microscopy (STEM) tools have been broadly employed for their characterization. Indeed, STEM provides a manifold characterization tool achieving insights on, not only the atomic structure and chemical composition of the analyzed materials, but also probing internal electric fields, plasmonic oscillations, light emission, band gap determination, electric field measurements, and many other properties. The emergence of new detectors and novel instrumental designs allowing the simultaneous collection of several signals render the perfect playground for the development of highly customized experiments specifically designed for the required analyses. This paper presents some of the most useful STEM techniques and several strategies and methodologies applied to address the specific analysis on semiconductors. STEM imaging, spectroscopies, 4D-STEM (in particular DPC), and in situ STEM are summarized, showing their potential use for the characterization of semiconductor nanostructured materials through recent reported studies.
PB MDPI
SN 2079-4991
YR 2022
FD 2022-02
LK http://hdl.handle.net/10498/26566
UL http://hdl.handle.net/10498/26566
LA eng
NO This work has been co-financed by the 2014-2020 ERDF Operational Programme and by the Department of Economy, Knowledge, Business and the University of the Regional Government of Andalusia. (FEDER-UCA18-106586). Co-funding from Junta de Andalucia (Research group INNANOMAT, ref. TEP-946) and UE is also acknowledged.
DS Repositorio Institucional de la Universidad de Cádiz
RD 15-nov-2026