用深度学习加速量子输运模拟，实现纳米电子器件的高效高精度仿真。
Deep Learning Accelerated Quantum Transport Simulations in Nanoelectronics: From Break Junctions to Field-Effect Transistors
- 结合深度学习与非平衡格林函数方法，预测紧束缚哈密顿量。
- 模拟10^4个断结结构，与实验导纳分布高度一致；支持超大体系（3×10⁴原子）。
- 适合需要高通量、大规模量子输运仿真的研究人员和芯片设计者。
量子输运模拟对理解与设计纳米电子器件至关重要，但长期存在精度与计算效率的权衡限制了其应用。我们提出DeePTB-NEGF框架，融合深度学习紧束缚哈密顿量预测与非平衡格林函数方法，在开放边界条件下实现2-3个数量级的加速，同时保持高精度。通过两个挑战性应用验证：对超过10⁴个快照的断结系统进行全尺度模拟，导纳分布与实验高度吻合；在实验尺寸下模拟碳纳米管场效应晶体管（CNT-FET），重现了41 nm沟道长度（约8000原子，3×10⁴轨道）器件的转移特性，并预测了180 nm碳纳米管（约3×10⁴原子，10⁵轨道）在零偏压下的传输谱，展现其大规模器件模拟能力。系统研究不同几何构型表明，模拟真实实验结构对精确预测至关重要。DeePTB-NEGF弥合了第一性原理精度与计算效率间的长期鸿沟，为高通量、大规模量子输运模拟提供可扩展工具，使此前难以触及的纳米尺度器件研究成为可能。
原文摘要 · Abstract (English)
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.
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