Model order reduction (MOR) is essential in integrated circuit design, particularly when dealing with large-scale electromagnetic models extracted from complex designs. The numerous passive elements introduced in these models pose significant challenges in the simulation process. MOR methods based on balanced truncation (BT) help address these challenges by producing compact reduced-order models (ROMs) that preserve the original model's input-output port behavior. In this work, we present an extended Krylov subspace-based BT approach with a frequency-aware convergence criterion and efficient implementation techniques for reducing large-scale models. Experimental results indicate that our method generates accurate and compact ROMs while achieving up to x22 smaller ROMs with similar accuracy compared to ANSYS RaptorX ROMs for large-scale benchmarks.

翻译：模型降阶在集成电路设计中至关重要，尤其是在处理从复杂设计中提取的大规模电磁模型时。这些模型中引入的大量无源元件给仿真过程带来了重大挑战。基于平衡截断的模型降阶方法通过生成能保持原始模型输入输出端口行为的紧凑降阶模型，有助于应对这些挑战。本文提出一种基于扩展Krylov子空间的平衡截断方法，该方法采用频率感知收敛准则与高效实现技术，用于大规模模型降阶。实验结果表明，对于大规模基准测试案例，相较于ANSYS RaptorX生成的降阶模型，本方法在保持相近精度的同时，能生成精确且紧凑的降阶模型，其规模最大可缩小22倍。