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<p>Piezotronics and piezo-phototronics are emerging fields for actuators, sensors, optoelectronic devices, energy-harvesting devices, and qubits. These fields involve materials that blend piezoelectric and semiconductor properties, including third-&#xa0;and fourth-generation semiconductors and low-dimensional materials, particularly two-dimensional materials that endure significant strain. Particularly in low-dimensional quantum materials, strain-induced polarization alters the quantum and charge-carrier transport characteristics, affecting the spin-&#xa0;and valley-dependent conductance, electronic density distribution, and polarization ratio. The electronic transport behavior of the bulk and edge states was studied by determining the conductance and electronic density distributions under various Fermi energies and stresses. Machine learning techniques aid in examining edge state transport properties and accurately predicting transport conductance, thereby impacting the design and optimization of high-performance quantum piezotronic devices. Recent theoretical advancements in piezotronics and piezo-phototronics have provided insights into quantum piezotronics and guided the development of piezotronic devices for metamaterials, quantum devices, and quantum computing.</p> Graphical abstract <p></p>

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Recent developments in theoretical explorations of piezotronics and piezo-phototronics

  • 期刊论文

  • 发表日期：

    2025年1月24日

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  • Yan Zhang,
  • Morten Willatzen,
  • Lijie Li

摘要

Piezotronics and piezo-phototronics are emerging fields for actuators, sensors, optoelectronic devices, energy-harvesting devices, and qubits. These fields involve materials that blend piezoelectric and semiconductor properties, including third- and fourth-generation semiconductors and low-dimensional materials, particularly two-dimensional materials that endure significant strain. Particularly in low-dimensional quantum materials, strain-induced polarization alters the quantum and charge-carrier transport characteristics, affecting the spin- and valley-dependent conductance, electronic density distribution, and polarization ratio. The electronic transport behavior of the bulk and edge states was studied by determining the conductance and electronic density distributions under various Fermi energies and stresses. Machine learning techniques aid in examining edge state transport properties and accurately predicting transport conductance, thereby impacting the design and optimization of high-performance quantum piezotronic devices. Recent theoretical advancements in piezotronics and piezo-phototronics have provided insights into quantum piezotronics and guided the development of piezotronic devices for metamaterials, quantum devices, and quantum computing.

Graphical abstract