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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Bibliographic Details
Published in:2025 62nd ACM/IEEE Design Automation Conference (DAC) pp. 1 - 7
Main Authors: Zeng, Lijie, Sun, Jiatai, Wu, Xiao, Niu, Dan, Wang, Tianshi, Lin, Yibo, Ye, Zuochang, Jin, Zhou
Format: Conference Proceeding
Language:English
Published: IEEE 22.06.2025
Subjects:
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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Summary: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) framework seeks to restore accuracy through an additional optimization step but is hampered by high memory consumption and slow convergence, particularly for large-scale problems. This paper presents G-SpNN, a novel GPU-accelerated framework that recasts the passivity-enforced macromodeling problem as a neural network training task. This approach significantly enhances both the speed and scalability of passivity enforcement. Experimental results show that G-SpNN achieves an average speedup of 7.63 \times in convergence compared to DAO, while reducing memory usage by two orders of magnitude. This enables G-SpNN to handle complex, high-port-count circuits with greater accuracy and efficiency, paving the way for robust high-frequency circuit simulations.
DOI:10.1109/DAC63849.2025.11133072