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) 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.