Variational capacitance modeling using orthogonal polynomial method
Proceedings of the 18th ACM Great Lakes symposium on VLSI
Statistical modeling and analysis of chip-level leakage power by spectral stochastic method
Proceedings of the 2009 Asia and South Pacific Design Automation Conference
PiCAP: a parallel and incremental capacitance extraction considering stochastic process variation
Proceedings of the 46th Annual Design Automation Conference
Statistical modeling and analysis of chip-level leakage power by spectral stochastic method
Integration, the VLSI Journal
Proceedings of the 2010 Asia and South Pacific Design Automation Conference
Variational capacitance extraction and modeling based on orthogonal polynomial method
IEEE Transactions on Very Large Scale Integration (VLSI) Systems
Stochastic analog circuit behavior modeling by point estimation method
Proceedings of the 2011 international symposium on Physical design
Proceedings of the 48th Design Automation Conference
A Fast Non-Monte-Carlo Yield Analysis and Optimization by Stochastic Orthogonal Polynomials
ACM Transactions on Design Automation of Electronic Systems (TODAES)
Statistical extraction and modeling of inductance considering spatial correlation
Analog Integrated Circuits and Signal Processing
SPECO: Stochastic Perturbation based Clock tree Optimization considering temperature uncertainty
Integration, the VLSI Journal
A parallel and incremental extraction of variational capacitance with stochastic geometric moments
IEEE Transactions on Very Large Scale Integration (VLSI) Systems
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Variations in the interconnect geometry of nanoscale ICs translate to variations in their performance. The resulting diminished accuracy in the estimates of performance at the design stage can lead to a significant reduction in the parametric yield. Thus, determining an accurate statistical description (e.g., moments, distribution, etc.) of the interconnect's response is critical for designers. In the presence of significant variations, device or interconnect model parameters such as wire resistance, capacitance, etc., need to modeled as random variables or as spatial random processes. The corner-based analysis is not accurate, and simulations based on sampling require long computation times due to the large number of parameters or random variables. This study proposes an efficient method of computing the stochastic response of interconnects. The technique models the stochastic response in an infinite dimensional Hilbert space in terms of orthogonal polynomial expansions. A finite representation is obtained by projecting the infinite series representation onto a finite dimensional subspace. The advantage of the proposed method is that it provides a functional representation of the response of the system in terms of the random variables that represent the process variations. The proposed algorithm has been implemented in a procedure called orthogonal polynomial expansions for response analysis (OPERA). Results from OPERA simulations on a number of design test cases match well with those from the classical Monte Carlo simulation program with integrated circuits emphasis (SPICE) and from perturbation methods. Additionally, OPERA shows good computational efficiency: speedup of up to two orders of magnitude have been observed over Monte Carlo SPICE simulations