Abstract
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. A quantum piezotronic transistor is proposed based on a circular quantum point contact of HgTe/CdTe topological insulator. Edge states and bulk states can be effectively controlled by strain-induced piezoelectric potential. Machine learning is employed to study edge-state transport, which can effectively predict transport conductance. • 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.
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