4 papers
Will Accurate Fields Mislead Photonic Design? FromGlobal Accuracy to Port Readout
Yitian Zhang, Yonghong chen, Youming Chen +6
Neural field surrogates can accelerate photonic design loops, but a surrogate that looks accurate in global field error can still mis-rank candidate devices when the final decision…
Learning from Scratch: Structurally-masked Transformer for Next Generation Lib-free Simulation
Junlang Huang, Hao Chen, Zhong Guan
This paper proposes a neural framework for power and timing prediction of multi-stage data path, distinguishing itself from traditional lib-based analytical methods dependent on dr…
Fusing Global and Local: Transformer-CNN Synergy for Next-Gen Current Estimation
Junlang Huang, Hao Chen, Li Luo +5
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, d…
S-Crescendo: A Nested Transformer Weaving Framework for Scalable Nonlinear System in S-Domain Representation
Junlang Huang, Hao Chen, Li Luo +5
Simulation of high-order nonlinear system requires extensive computational resources, especially in modern VLSI backend design where bifurcation-induced instability and chaos-like…