Model order reduction (MOR) is an important step in the design process of integrated circuits. Specifically, the electromagnetic models extracted from modern complex designs result in a large number of passive elements that introduce limitations in the simulation process. MOR techniques based on balanced truncation (BT) can overcome these limitations by producing compact reduced-order models (ROMs) that approximate the behavior of the original models at the input/output ports. In this paper, we present a low-rank BT method that exploits the extended Krylov subspace and efficient implementation techniques for the reduction of large-scale models. Experimental evaluation on a diverse set of analog and mixed-signal circuits with millions of elements indicates that up to x5.5 smaller ROMs can be produced with similar accuracy to ANSYS RaptorX ROMs.

翻译：模型降阶是集成电路设计过程中的重要环节。具体而言，从现代复杂设计中提取的电磁模型会产生大量无源元件，这些元件在仿真过程中带来诸多限制。基于平衡截断的模型降阶技术能够通过生成紧凑的降阶模型来克服这些限制，该模型在输入/输出端口处近似原始模型的行为。本文提出一种低秩平衡截断方法，该方法利用扩展Krylov子空间和高效的实现技术来实现大规模模型的降阶。对包含数百万元件的多种模拟及混合信号电路的实验评估表明，与ANSYS RaptorX降阶模型相比，本方法可生成尺寸缩小达5.5倍的降阶模型，同时保持相似的精度。