Efficient Recycling Subspace Truncation Method for Periodic Small-Signal Analysis

Periodic small-signal analysis is crucial but timeconsuming in RF simulation, since it may deal with many frequency points. While the Krylov subspace recycling method has greatly accelerated the simulation, the increasing memory cost in large-scale RF simulation has become a new bottleneck. A remedy for memory shortage is to restart the recycling algorithm, but may cause excessive extra iterations. To address this issue, this paper outlines a framework of recycling subspace truncation method for periodic small-signal analysis, provided with an efficient initial guess choice method and a Floquet-based subspace truncation strategy. Numerical results show that compared to the existing methods, the proposed method achieves up to $2.5 \times$ speedup in the same memory cost.