用Transformer+KAN模型，高效精准模拟难处理的电路瞬态响应。
Stiff Circuit System Modeling via Transformer
- 结合Crossformer与KAN，捕捉电路时间序列特征。
- 在ADC电路数据上，训练时间更短，误差显著降低。
- 适合需要快速高精度电路仿真的工程师和研究者。
精确高效的电路行为建模是现代电子设计自动化的核心。对于各类电路而言，刚性电路（stiff circuits）的建模仍面临挑战。本文提出一种新方法，采用当前最先进的时序预测Transformer模型Crossformer，并结合科尔莫戈罗夫-阿诺德网络（KANs），以建模刚性电路的瞬态行为。通过利用Crossformer的时间表征能力以及KAN增强的特征提取能力，该方法在多种输入条件下均能实现更高保真度的电路响应预测。基于模拟器SPICE生成的模拟数字转换器（ADC）电路数据集的实验评估表明，该方法在显著减少训练时间的同时，有效降低了误差率。
原文摘要 · Abstract (English)
Accurate and efficient circuit behavior modeling is a cornerstone of modern electronic design automation. Among different types of circuits, stiff circuits are challenging to model using previous frameworks. In this work, we propose a new approach using Crossformer, which is a current state-of-the-art Transformer model for time-series prediction tasks, combined with Kolmogorov-Arnold Networks (KANs), to model stiff circuit transient behavior. By leveraging the Crossformer's temporal representation capabilities and the enhanced feature extraction of KANs, our method achieves improved fidelity in predicting circuit responses to a wide range of input conditions. Experimental evaluations on datasets generated through SPICE simulations of analog-to-digital converter (ADC) circuits demonstrate the effectiveness of our approach, with significant reductions in training time and error rates.
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