Spatial variation decomposition via sparse regression
Wangyang Zhang, Karthik Balakrishnan, et al.
ICICDT 2012
We show how advances in the handling of correlated interval representations of range uncertainty can be used to approximate the mass of a probability density function as it moves through numerical operations and, in particular, to predict the impact of statistical manufacturing variations on linear interconnect. We represent correlated statistical variations in resistanceinductance-capacitance (RLC) parameters as sets of correlated intervals and show how classical model-order reduction methods-asymptotic waveform evaluation and passive reducedorder interconnect macromodeling algorithm-can be retargeted to compute interval-valued, rather than scalar-valued, reductions. By applying a simple statistical interpretation and sampling to the resulting compact interval-valued model, we can efficiently estimate the impact of variations on the original circuit. Results show that the technique can predict mean delay and standard deviation with errors between 5% and 10% for correlated RLC parameter variations up to 35%. © 2007 IEEE.
Wangyang Zhang, Karthik Balakrishnan, et al.
ICICDT 2012
Wangyang Zhang, Karthik Balakrishnan, et al.
IEEE TCADIS
Saul A. Kravitz, Randal E. Bryant, et al.
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Patrick Groeneveld, Rob A. Rutenbar, et al.
DAC 2009