Neural network (NN)-based transistor compact modeling has recently emerged as a transformative solution for accelerating device modeling and SPICE circuit simulations. However, conventional NN architectures, despite their widespread adoption in state-of-the-art methods, primarily function as black-box problem solvers. This lack of interpretability significantly limits their capacity to extract and convey meaningful insights into learned data patterns, posing a major barrier to their broader adoption in critical modeling tasks. This work introduces, for the first time, Kolmogorov-Arnold network (KAN) for the transistor - a groundbreaking NN architecture that seamlessly integrates interpretability with high precision in physics-based function modeling. We systematically evaluate the performance of KAN and Fourier KAN for FinFET compact modeling, benchmarking them against the golden industry-standard compact model and the widely used MLP architecture. Our results reveal that KAN and FKAN consistently achieve superior prediction accuracy for critical figures of merit, including gate current, drain charge, and source charge. Furthermore, we demonstrate and improve the unique ability of KAN to derive symbolic formulas from learned data patterns - a capability that not only enhances interpretability but also facilitates in-depth transistor analysis and optimization. This work highlights the transformative potential of KAN in bridging the gap between interpretability and precision in NN-driven transistor compact modeling. By providing a robust and transparent approach to transistor modeling, KAN represents a pivotal advancement for the semiconductor industry as it navigates the challenges of advanced technology scaling.

翻译：基于神经网络（NN）的晶体管紧凑建模近年来已成为加速器件建模和SPICE电路仿真的变革性解决方案。然而，尽管传统的神经网络架构在最先进的方法中得到广泛应用，但它们主要充当黑盒问题求解器。这种可解释性的缺乏极大地限制了其提取和传达学习数据模式中有意义见解的能力，对其在关键建模任务中的更广泛采用构成了主要障碍。本研究首次将Kolmogorov-Arnold网络（KAN）引入晶体管建模领域——这是一种突破性的神经网络架构，在基于物理的函数建模中，将可解释性与高精度无缝集成。我们系统评估了KAN和傅里叶KAN（Fourier KAN）在FinFET紧凑建模中的性能，并以行业黄金标准紧凑模型和广泛使用的MLP架构为基准进行对比。结果表明，KAN和FKAN在关键性能指标（包括栅极电流、漏极电荷和源极电荷）的预测精度上始终表现更优。此外，我们展示并提升了KAN从学习数据模式中推导符号公式的独特能力——这一能力不仅增强了可解释性，也促进了深入的晶体管分析与优化。本工作凸显了KAN在弥合神经网络驱动的晶体管紧凑建模中可解释性与精度之间鸿沟的变革潜力。通过为晶体管建模提供一种稳健且透明的方法，KAN代表了半导体行业在应对先进技术缩放挑战过程中的关键性进展。