Model order reduction of electronic devices and on-chip interconnects plays an important role in determining the performance of very large scale IC (Integrated Circuit) designs. The ever shrinking process technology continues to increase complexity of these systems with the current Intel device technology node standing at 10 nm. In the analysis of high-speed IC interconnects, the reduced model is often required to approximate the original system in the desired frequency range. In this paper, an efficient Statistically Inspired Multi-shift Arnoldi (SIMA) Method is proposed, which dynamically selects normally distributed shifts to determine the projection matrix. The SIMA is implemented by developing an iterative algorithm for approximation of large-scale systems as an eigenvalue problem using statistically generated interpolation points. The motivation of selecting these points weighted with the Gaussian kernel is derived from the fact that there exist many natural phenomenon, which follow normal distribution. Simulation results have shown better accuracy for the proposed method as compared to other existing model order reduction techniques.

电子设备和片上互连的模型阶数减少在确定超大规模 IC（集成电路）设计的性能方面起着重要作用。不断缩小的工艺技术继续增加这些系统的复杂性，当前英特尔设备技术节点处于 10 纳米。在分析高速 IC 互连时，通常需要简化模型以在所需频率范围内逼近原始系统。在本文中，提出了一种有效的统计启发多位移 Arnoldi (SIMA) 方法，该方法动态选择正态分布的位移来确定投影矩阵。SIMA 是通过开发迭代算法来实现的，该算法使用统计生成的插值点将大规模系统逼近为特征值问题。选择这些高斯核加权的点的动机是因为存在许多服从正态分布的自然现象。仿真结果表明，与其他现有的模型降阶技术相比，所提出的方法具有更好的准确性。