Deep Learning Accelerated Quantum Transport Simulations in Nanoelectronics: From Break Junctions to Field-Effect Transistors

Abstract

Quantum transport simulations are essential for understanding and designing nanoelectronic devices, yet the long-standing trade-off between accuracy and computational efficiency has limited their practical applications. We present DeePTB-NEGF, an integrated framework combining deep learning tight-binding Hamiltonian prediction with non-equilibrium Green's Function methodology to enable accurate quantum transport simulations in open boundary conditions with 2-3 orders of magnitude acceleration. We demonstrate DeePTB-NEGF through two challenging applications: comprehensive break junction simulations with over $10^4$ snapshots, showing excellent agreement with experimental conductance histograms; and carbon nanotube field-effect transistors (CNT-FET) at experimental dimensions, reproducing measured transfer characteristics for a 41 nm channel CNT-FET ($\sim 8000$ atoms, $3\times10^4$ orbitals) and predicting zero-bias transmission spectra for a 180 nm CNT ($\sim 3\times 10^4$ atoms, $10^5$ orbitals), showcasing the framework's capability for large-scale device simulations. Our systematic studies across varying geometries confirm the necessity of simulating realistic experimental structures for precise predictions. DeePTB-NEGF bridges the longstanding gap between first-principles accuracy and computational efficiency, providing a scalable tool for high-throughput and large-scale quantum transport simulations that enables previously inaccessible nanoscale device investigations.