[2510.24727] Stiff Circuit System Modeling via Transformer

Computer Science > Computational Engineering, Finance, and Science arXiv:2510.24727 (cs) [Submitted on 6 Oct 2025 (v1), last revised 23 Mar 2026 (this version, v2)] Title:Stiff Circuit System Modeling via Transformer Authors:Weiman Yan, Yi-Chia Chang, Wanyu Zhao View a PDF of the paper titled Stiff Circuit System Modeling via Transformer, by Weiman Yan and 2 other authors View PDF HTML (experimental) Abstract: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. Subjects: Computational Engineering, Finance, and Science (cs.CE); Machine Learning (cs.LG) Cite as: arXiv:2510.24727 [cs.CE]   (or arXiv:2510.24727v2 [cs.CE] for this version)   https://d...