MLACA With Modified Grouping Strategy for Efficient Superconducting Circuit Analysis

Electromagnetic analysis of superconducting integrated circuits is routinely required for inductance extraction. FastHenry is a well-known, applicable magnetoquasistatic analysis tool. It is accelerated with the multilevel fast multipole algorithm (MLFMA). A multilevel adaptive cross approximation solver with singular value decomposition recompression (MLACA-SVD) was recently proposed for FastHenry, as an alternative to the MLFMA. It was found to be more efficient, with regards to accuracy versus required memory. It uses the same octree grouping as FastHenry's MLFMA. Here, two modified grouping strategies are proposed to further improve MLACA-SVD efficiency by compressing interactions between larger groups, while maintaining scaling performance consistent with valid admissibility of interactions. The strategies are denoted “shell merging” and “wall merging.” Results show that the MLACA-SVD solver with merging requires about four times less memory than FastHenry's MLFMA, for similar accuracy. Shell merging is found to be slightly superior.