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.

翻译：高效精确的电路行为建模是现代电子设计自动化的基石。在各类电路中，刚性电路因传统框架难以建模而具有挑战性。本文提出一种结合当前最先进的时间序列预测Transformer模型——Crossformer与柯尔莫哥洛夫-阿诺德网络（KANs）的新方法，用于建模刚性电路的瞬态行为。通过利用Crossformer的时间表征能力与KANs的增强特征提取功能，我们的方法在预测电路对广泛输入条件的响应时，实现了更高的保真度。基于模数转换器（ADC）电路SPICE仿真生成的数据集进行的实验评估表明，该方法在显著降低训练时间与错误率的同时具备有效性。