用混合模型直接预测电流波形，比传统方法更准更快
Fusing Global and Local: Transformer-CNN Synergy for Next-Gen Current Estimation
- 结合Transformer全局建模与CNN局部特征提取
- 误差仅0.0098，远超传统SPICE仿真
- 适合40nm到3nm工艺的电流与功耗分析
本文提出一种融合Transformer与CNN的混合模型，用于预测信号线中的电流波形。不同于依赖标准单元驱动器或RC负载简化模型的传统方法（如电流源模型、驱动器线性表示、波形函数拟合或等效负载电容法），该模型无需复杂的牛顿迭代求解过程，直接利用Transformer强大的序列建模能力预测电流响应。混合架构有效结合了Transformer的全局特征捕捉能力和CNN的局部特征提取优势，显著提升电流波形预测精度。实验结果表明，与传统SPICE仿真相比，该算法误差仅为0.0098。结果验证了其在信号线电流波形、时序分析和功耗评估方面的卓越性能，适用于从40nm到3nm的多种技术节点。
原文摘要 · Abstract (English)
This paper presents a hybrid model combining Transformer and CNN for predicting the current waveform in signal lines. Unlike traditional approaches such as current source models, driver linear representations, waveform functional fitting, or equivalent load capacitance methods, our model does not rely on fixed simplified models of standard-cell drivers or RC loads. Instead, it replaces the complex Newton iteration process used in traditional SPICE simulations, leveraging the powerful sequence modeling capabilities of the Transformer framework to directly predict current responses without iterative solving steps. The hybrid architecture effectively integrates the global feature-capturing ability of Transformers with the local feature extraction advantages of CNNs, significantly improving the accuracy of current waveform predictions. Experimental results demonstrate that, compared to traditional SPICE simulations, the proposed algorithm achieves an error of only 0.0098. These results highlight the algorithm's superior capabilities in predicting signal line current waveforms, timing analysis, and power evaluation, making it suitable for a wide range of technology nodes, from 40nm to 3nm.
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