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<p>Non-equilibrium electronic quantum transport is crucial for existing and envisioned electronic, optoelectronic, and spintronic devices. Encompassing atomistic to mesoscopic length scales in the same nonequilibrium device simulations has been challenging due to the computational cost of high-fidelity coupled multiphysics and multiscale requirements. In this work, we present ELEQTRONeX (<b>ELE</b>ctrostatic <b>Q</b>uantum <b>TR</b>ansport modeling <b>O</b>f <b>N</b>anomaterials at <b>eX</b>ascale), a massively parallel GPU-accelerated framework for self-consistently solving the nonequilibrium Green’s function formalism and electrostatics in complex device geometries. By customizing algorithms for GPU multithreading, we achieve significant improvement in computational time, and excellent scaling on up to 512 GPUs and billions of spatial grid cells. We validate our code by computing band structures, current-voltage characteristics, conductance, and drain-induced barrier lowering for various 3D configurations of carbon nanotube field-effect transistors, and demonstrate its suitability for complex device/material geometries where periodic approaches are not feasible, such as arrays of misaligned carbon nanotubes requiring fully 3D simulations.</p>

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ELEQTRONeX: A GPU-accelerated exascale framework for non-equilibrium quantum transport in nanomaterials

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年4月24日

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  • Saurabh S. Sawant,
  • François Léonard,
  • Zhi Yao,
  • Andrew Nonaka

摘要

Non-equilibrium electronic quantum transport is crucial for existing and envisioned electronic, optoelectronic, and spintronic devices. Encompassing atomistic to mesoscopic length scales in the same nonequilibrium device simulations has been challenging due to the computational cost of high-fidelity coupled multiphysics and multiscale requirements. In this work, we present ELEQTRONeX (ELEctrostatic Quantum TRansport modeling Of Nanomaterials at eXascale), a massively parallel GPU-accelerated framework for self-consistently solving the nonequilibrium Green’s function formalism and electrostatics in complex device geometries. By customizing algorithms for GPU multithreading, we achieve significant improvement in computational time, and excellent scaling on up to 512 GPUs and billions of spatial grid cells. We validate our code by computing band structures, current-voltage characteristics, conductance, and drain-induced barrier lowering for various 3D configurations of carbon nanotube field-effect transistors, and demonstrate its suitability for complex device/material geometries where periodic approaches are not feasible, such as arrays of misaligned carbon nanotubes requiring fully 3D simulations.