Kolmogorov–Arnold Network for Transistor Compact Modeling

Neural network (NN)‐based transistor compact modeling is a transformative solution for accelerating device modeling and SPICE circuit simulations. However, conventional NN architectures 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 Kolmogorov–Arnold network (KAN) for the transistor—a groundbreaking NN architecture that seamlessly integrates interpretability with high precision in physics‐based function modeling. The performance of KAN and Fourier KAN (FKAN) is systematically evaluated for fin field‐effect transistor (FinFET) compact modeling, benchmarking them against the golden industry‐standard compact model and the widely used multi‐layer perceptron architecture. The results reveal that KAN and FKAN consistently achieve superior prediction accuracy for critical figures of merit, including gate current ( I D ), drain charge ( Q D ), and source charge ( Q S ). Furthermore, the unique ability of KAN to derive symbolic formulas from learned data patterns is demonstrated and improved—a capability that not only enhances interpretability but also facilitates in‐depth transistor analysis and optimization. Additionally, we identify challenges inherent to KAN and FKAN architectures, which limit their ability to achieve a general‐purpose SPICE simulation suitability.