A New Spatial Correlation Model Based on the Distributed RC-∏ Model

2014 ◽  
Vol 989-994 ◽  
pp. 2204-2207
Author(s):  
Xiao Xiao Liu ◽  
Jing Bo Shao ◽  
Ling Ling Zhao

To solve the crosstalk noise question in deep-submicron technologies, a new spatial correlation model based on the distributed RC-π model is proposed in this paper. Quiet aggressor net and tree branch reduction techniques are introduced to the distributed RC-π model, and a new spatial correlation model of both Gaussian and non-Gaussian process variations among segments is created. Experimental results show that our method maintains the efficiency of past approaches, and significantly improves on their accuracy.

1983 ◽  
Vol 27 ◽  
Author(s):  
D.E. Aspnes ◽  
K.K. Tiong ◽  
P.M. Amirtharaj ◽  
F.H. Pollak

ABSTRACTThe red shift and asymmetric broadening of the LO phonon mode of ion-implanted GaAs are both described quantitatively by a spatial correlation model based on a damage-induced relaxation of the momentum selection rule previously used by Richter, Wang, and Ley to describe similar effects in microcrystalline Si. The success of the model for a qualitatively different disorder microstructure suggests it may be possible to evaluate average sizes of crystallographically perfect regions in semiconductors from the phonon lineshapes of their Raman spectra.


2014 ◽  
Vol 989-994 ◽  
pp. 2647-2650
Author(s):  
Xiao Xiao Liu ◽  
Jing Bo Shao ◽  
Ling Ling Zhao

To solve the crosstalk noise question in deep-submicron technologies, an efficient methodology for estimating interconnect crosstalk noise is proposed in this paper. PCA and ICA techniques are applied to reduce correlations of process variations, and moment matching scheme is used to obtain the PDF of crosstalk noise in victim coupled with multiple aggressors. Experimental results show that our method maintains the efficiency of past approaches, and significantly improves on their accuracy.


2021 ◽  
pp. 1-13
Author(s):  
Haitao Liu ◽  
Yew-Soon Ong ◽  
Ziwei Yu ◽  
Jianfei Cai ◽  
Xiaobo Shen

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