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A Spectral Stochastic Collocation Method for Variation-Aware Capacitance Extraction of Interconnects under Nanometer Process Technology
| Content Provider | Semantic Scholar |
|---|---|
| Author | Zhu, Hengliang Zeng, Xuan Xue, Jintao Zhou, Dian |
| Copyright Year | 2007 |
| Abstract | In this paper, a Spectral Stochastic Collocation Method (SSCM) is proposed for variation-aware capacitance extraction of interconnects for nanometer process technolgy. The proposed SSCM has several advantages over the existing methods. Firstly, compared with the PFA (Principal Factor Analysis) modeling of geometric variations, the K-L (KarhunenLoeve) expansion involved in SSCM can be independent of the discretization of conductors, thus significantly reduces t he computation cost. Secondly, compared with the perturbation method, the stochastic spectral method based on Homogeneous Chaos expansion has optimal (exponential) convergence rate, whi ch makes SSCM applicable for most geometric variation cases. Furthermore, Sparse Grid combined with a MST (Minimum Spanning Tree) representation is proposed to reduce the num ber of sampling points and the computation time for capacitance extraction at each sampling point. Numerical experiments have demonstrated that SSCM can achieve higher accuracy and fast er convergence rate compared with the perturbation method. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | http://math2.uncc.edu/~wcai/SSCM_trans.pdf |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
