慕尼黑工业大学用神经网络加速 3nm 纳米片 SPICE 仿真
Neural Networks Speed 3nm Nanosheet SPICE Simulation (TU Munich)
慕尼黑工业大学研究人员开发出基于神经网络的 3nm 纳米片晶体管模型，用于 SPICE 仿真和标准单元库表征。相比业界标准的 Verilog-A 实现（BSIM-CMG），NN 模型在时延和总功耗评估上误差分别低于 0.11% 和 0.76%，标准单元库表征耗时最多减少 8 倍，可准确预测漏电流和多种电荷。
Researchers at Technical University of Munich published “NN-Based Transistor Modeling for Ultra-Fast SPICE Simulation and Standard Cell Library Characterization.”
Abstract:
“Neural network (NN)-based transistor models have proven to be a promising solution to accelerate device modeling. Although these models demonstrated remarkable speedup in circuit simulations, they were not rigorously tested in complex tasks within standard EDA tool flows for chip design, which require very high transistor model precision to provide accurate results and converge properly. To investigate their full potential and promise, we develop NN-based transistor models that accurately predict the drain current and various charges for a 3nm nanosheet, and employ the models for a standard cell library characterization. This enables us to evaluate the accuracy and speedup under a very wide range of SPICE simulations, benchmarking our models against an industry-standard implementation. Since standard cell library characterization involves thousands of SPICE simulations, it serves as a great benchmark for transistor model quality and consistency. To further evaluate model accuracy, we also perform analog- and digital-level simulations for various complex circuits. Our experiments reveal that NN-based models are a valid alternative to existing transistor compact models, capable of producing accurate delay and power estimates with a much higher speed and convergence time compared to the conventional Verilog-A-based industry-standard (BSIM-CMG) transistor compact model. Our NNs achieve sub-0.11% and 0.76% errors for delay and total power evaluation on a wide range of circuits, while taking up to 8 times less time during the standard cell library characterization. Additionally, we provide comprehensive information and guidance on NN accuracy, unveiling the relationship between NN size and its effect on circuit simulation precision.”
Find the technical paper here. October 2026
Novkin, Rodion, and Hussam Amrouch. “NN-Based Transistor Modeling for Ultra-Fast SPICE Simulation and Standard Cell Library Characterization.” arXiv, October 2026. https://doi.org/10.48550/arXiv.2610.31024. License
来源： Semiconductor Engineering 芯片与封装 · semiengineering.com