发表机构
Università di Pisa（比萨大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出一种多尺度模拟框架，结合第一性原理与NEGF输运计算，用于预测二维铁电材料场效应晶体管的性能，并以磷化铟单层为例，无需经验参数即可重现磁滞现象。
AI 中文摘要
铁电材料在低功耗、高速电子器件及新兴的神经形态计算架构中极具吸引力。然而，传统块体（三维）铁电体在纳米尺度上面临严重的物理微缩限制，这使得研究焦点转向二维（2D）铁电单层材料。精确的器件级性能预测对于加速这些新型材料的实验测试和筛选至关重要。在本工作中，我们提出了一种多尺度模拟框架，该框架将第一性原理密度泛函理论与非平衡格林函数（NEGF）输运计算相衔接。以二维磷化铟单层作为案例研究，我们的方法利用了一个连续插值的、依赖于极化的哈密顿量，并将其嵌入到自洽的泊松-NEGF求解器中。该模型捕捉了离子运动与电子输运之间的动态相互作用，无需经验参数即可自然重现宏观磁滞回线和存储窗口。这一预测流程为评估和优化下一代二维铁电场效应晶体管提供了一种计算高效的工具。
英文摘要
Ferroelectric materials are highly attractive for low-power, high-speed electronics and emerging neuromorphic computing architectures. However, the severe physical scaling limits of conventional bulk (3D) ferroelectrics at the nanoscale have shifted attention towards two-dimensional (2D) ferroelectric monolayers. Accurate device-level performance predictions are essential to accelerate the experimental testing and screening of these novel materials. In this work, we present a multiscale simulation framework that bridges first-principles density functional theory with Non-Equilibrium Green's Function (NEGF) transport calculations. Using a 2D Indium Phosphide monolayer as a case study, our approach leverages a continuously interpolated, polarization-dependent Hamiltonian embedded within a self-consistent Poisson-NEGF solver. The model captures the dynamic interplay between ion movement and electronic transport, naturally reproducing macroscopic hysteresis loops and memory windows without empirical parameters. This predictive pipeline provides a computationally efficient tool to evaluate and optimize next-generation 2D ferroelectric field-effect transistors.
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