标题： 集成物理的神经网络用于场效应晶体管量子输运预测 显示英文标题

标题： Physics-integrated Neural Network for Quantum Transport Prediction of Field-effect Transistor

摘要： 基于量子力学的输运模拟对于设计超短沟道场效应晶体管（FET）具有重要意义，因为它能够理解物理机制，但面临计算强度高的主要挑战。传统的机器学习有望加速FET设计的优化，但其在该领域的应用受到高保真数据集和物理知识整合不足的限制。在这里，我们引入了一个集成物理知识的神经网络框架，使用自行开发的高保真数据库预测亚5纳米栅极全绕（GAA）FET的输运曲线。数据库中的输运曲线来自文献和我们的第一性原理计算。除了硅之外，我们还包含了不同结构相的砷化铟、磷化铟和硒纳米线作为FET沟道材料。然后，我们构建了一个集成物理知识的超向量神经网络（PHVNN），其中添加了五个新的物理特征作为输入以预测输运特性，实现了足够低的平均绝对误差0.39。特别是，约98%的电流预测残差在一个数量级以内。使用PHVNN，我们高效地筛选出了对称的p型GAA FET，它们与n型FET具有相同的性能指标，这对于均质CMOS电路的制造至关重要。最后，我们的自动微分分析提供了对PHVNN的可解释见解，这突出了我们新输入参数的重要贡献，并提高了PHVNN的可靠性。我们的方法提供了一种有效的方法，用于快速筛选合适的GAA FET，有望加速下一代电子器件的设计过程。 显示英文摘要

摘要： Quantum-mechanics-based transport simulation is of importance for the design of ultra-short channel field-effect transistors (FETs) with its capability of understanding the physical mechanism, while facing the primary challenge of the high computational intensity. Traditional machine learning is expected to accelerate the optimization of FET design, yet its application in this field is limited by the lack of both high-fidelity datasets and the integration of physical knowledge. Here, we introduced a physics-integrated neural network framework to predict the transport curves of sub-5-nm gate-all-around (GAA) FETs using an in-house developed high-fidelity database. The transport curves in the database are collected from literature and our first-principles calculations. Beyond silicon, we included indium arsenide, indium phosphide, and selenium nanowires with different structural phases as the FET channel materials. Then, we built a physical-knowledge-integrated hyper vector neural network (PHVNN), in which five new physical features were added into the inputs for prediction transport characteristics, achieving a sufficiently low mean absolute error of 0.39. In particular, ~98% of the current prediction residuals are within one order of magnitude. Using PHVNN, we efficiently screened out the symmetric p-type GAA FETs that possess the same figures of merit with the n-type ones, which are crucial for the fabrication of homogeneous CMOS circuits. Finally, our automatic differentiation analysis provides interpretable insights into the PHVNN, which highlights the important contributions of our new input parameters and improves the reliability of PHVNN. Our approach provides an effective method for rapidly screening appropriate GAA FETs with the prospect of accelerating the design process of next-generation electronic devices.