Edge-state transport in circular quantum point contact quantum piezotronic transistors
- 1. School of Physics, University of Electronic Science and Technology of China, Chengdu 610054 (China)
- 2. Multidisciplinary Nanotechnology Centre, College of Engineering, Swansea University, Swansea SA1 8EN (United Kingdom)
- 3. Beijing Institute of Nanoenergy and Nanosystems, Chinese Academy of Sciences, National Center for Nanoscience and Technology (NCNST), Beijing 100083 (China)
Description
Highlights: • Piezotronic effect can effectively control bulk and edge states. • The radius of a circular quantum point contact is modulated by strain-induced piezoelectric potential. • Machine learning is applied to predict transport conductance of quantum piezotronic devices. Quantum piezotronic transistor is studied based on HgTe/CdTe topological insulator with a circular quantum point contact. The radius of the circular region is modulated by strain-induced piezoelectric potential. The electronic transport behavior of the edge and bulk states is explored by calculating the conductance and electronic density distribution under different Fermi energies and strains. Transport property of edge states is studied by machine learning method and the transport conductance can be effectively predicted. These results show that the neural network can be used for obtaining electronic transport properties, and it has great potential for optimizing and designing high-performance quantum piezotronic devices.
Availability note (English)
Available from http://dx.doi.org/10.1016/j.nanoen.2021.106002Additional details
Identifiers
- DOI
- 10.1016/j.nanoen.2021.106002;
- PII
- S2211285521002603;
Publishing Information
- Journal Title
- Nano Energy (Print)
- Journal Volume
- 85
- Journal Page Range
- vp.
- ISSN
- 2211-2855
INIS
- Country of Publication
- Netherlands
- Country of Input or Organization
- International Atomic Energy Agency (IAEA)
- INIS RN
- 54014436
- Subject category
- S97: MATHEMATICAL METHODS AND COMPUTING; S75: CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND SUPERFLUIDITY;
- Descriptors DEI
- CADMIUM TELLURIDES; DENSITY; DESIGN; ELECTRIC CONTACTS; MACHINE LEARNING; MERCURY TELLURIDES; NEURAL NETWORKS; OPTIMIZATION; PERFORMANCE; PIEZOELECTRICITY; TRANSISTORS
- Descriptors DEC
- ALGORITHMS; ARTIFICIAL INTELLIGENCE; CADMIUM COMPOUNDS; CHALCOGENIDES; ELECTRICAL EQUIPMENT; ELECTRICITY; EQUIPMENT; LEARNING; MATHEMATICAL LOGIC; MERCURY COMPOUNDS; PHYSICAL PROPERTIES; SEMICONDUCTOR DEVICES; TELLURIDES; TELLURIUM COMPOUNDS
Optional Information
- Copyright
- Copyright (c) 2021 Elsevier Ltd. All rights reserved.