<ul><li>This paper presents a hybrid model combining Transformer and CNN to predict current waveforms in signal lines.</li><li>The model does not rely on fixed simplified models and replaces the complex process used in traditional SPICE simulations.</li><li>The hybrid architecture combines the global feature-capturing ability of Transformers with the local feature extraction advantages of CNNs.</li><li>Experimental results demonstrate that the proposed algorithm achieves an error of only 0.0098, improving the accuracy of current waveform predictions.</li></ul>

Fusing Global and Local: Transformer-CNN Synergy for Next-Gen Current Estimation

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