A new method is presented for high-dimensional variability analysis based on two main concepts, namely, node tearing for parameter partitioning and sparse grid interpolation. Node tearing is used to localize the parameters and, thus, reducing the number of stochastic parameters within the subcircuits and sparse grids reduce the required number of samples for a targeted accuracy. MC analysis of the overall circuit is carried out using interface equations of a much smaller dimension than the original circuit equations. Pertinent computational results are presented to validate the efficiency and accuracy of the proposed method.

中文翻译：

---

使用参数空间划分的不确定性量化

提出了一种基于两个主要概念的高维变异性分析新方法，即用于参数划分的节点撕裂和稀疏网格插值。节点撕裂用于定位参数，因此，减少了子电路内的随机参数的数量，而稀疏网格减少了目标精度所需的样本数量。使用比原始电路方程小得多的接口方程进行整个电路的MC分析。给出了相关的计算结果，以验证所提出方法的效率和准确性。