G-SpNN: GPU-Accelerated Passivity Enforcement for S-Parameter Modeling with Neural Networks

The increasing complexity of high-frequency circuits calls for efficient and accurate passive macromodeling techniques. Existing passivity enforcement methods, including those in commercial tools, often encounter convergence issues or compromise accuracy. The Domain-Alternated Optimization (DAO) fra...

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Vydané v:	2025 62nd ACM/IEEE Design Automation Conference (DAC) s. 1 - 7
Hlavní autori:	Zeng, Lijie, Sun, Jiatai, Wu, Xiao, Niu, Dan, Wang, Tianshi, Lin, Yibo, Ye, Zuochang, Jin, Zhou
Médium:	Konferenčný príspevok..
Jazyk:	English
Vydavateľské údaje:	IEEE 22.06.2025
Predmet:	Accuracy Convergence GPU Acceleration Graphics processing units Integrated circuit modeling Memory management Neural Network Neural networks Optimization Passivity Enforcement S-Parameter Macromodeling Scalability Scattering parameters Training
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