工艺偏差下模拟电路模块行为级建模的稀疏网格随机配置法

为建立考虑工艺参数随机变化情况下的模拟电路模块的行为级模型,提出了基于稀疏网格的随机配置建模方法.首先,与传统的需要大量采点的蒙特卡罗方法相比,随机配置方法具有指数收敛的特性,大大降低了计算复杂度.其次,为了避免通常用于多维空间的直接张量积随机配置法所带来的采点数随空间维度指数增长的缺点,采用稀疏网格采点技术,在保证建模精度的同时大幅度减少了配置点数目,从而进一步降低了随机配置法的计算复杂度.

Sparse Grid Based Stochastic Collocation Method for Analog Building Block Behavioral Modeling under Process Variations

The Sparse Grid Based Stochastic Collocation Method is proposed to model the analog circuit with process variations.First,compared with the traditional Monte Carlo method which needs a large amount of sampling points,the Stochastic Collocation Method has the exponential convergence rate,which reduces the computational complexity significantly.Second,compared with direct tensor product in multi-dimensional space,which suffers from the exponential increase of the number of sampling points with the number of variables,the Sparse Grid technique decreases the number of collocation points dramatically while the accuracy is guaranteed,and further reduces the computational complexity.