Performance-constrained worst-case variability minimization of VLSI circuits
DAC '93 Proceedings of the 30th international Design Automation Conference
Latin Hypercube Sampling Monte Carlo Estimation of AverageQuality Index for Integrated Circuits
Analog Integrated Circuits and Signal Processing - Special issue: analog design issues in digital VSLI circuits and systems
Latin supercube sampling for very high-dimensional simulations
ACM Transactions on Modeling and Computer Simulation (TOMACS) - Special issue on uniform random number generation
A generalized discrepancy and quadrature error bound
Mathematics of Computation
On the L2-discrepancy for anchored boxes
Journal of Complexity
Practical, fast Monte Carlo statistical static timing analysis: why and how
Proceedings of the 2008 IEEE/ACM International Conference on Computer-Aided Design
Analog Design Centering and Sizing
Analog Design Centering and Sizing
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
ACM Transactions on Design Automation of Electronic Systems (TODAES)
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Yield estimation for analog integrated circuits remains a time-consuming operation in variation-aware sizing. State-of-the-art statistical methods such as ranking-integrated Quasi-Monte-Carlo (QMC), suffer from performance degradation if the number of effective variables is large (as typically is the case for realistic analog circuits). To address this problem, a new method, called AYLeSS, is proposed to estimate the yield of analog circuits by introducing Latin Supercube Sampling (LSS) technique from the computational statistics field. Firstly, a partitioning method is proposed for analog circuits, whose purpose is to appropriately partition the process variation variables into low-dimensional sub-groups fitting for LSS sampling. Then, randomized QMC is used in each sub-group. In addition, the way to randomize the run order of samples in Latin Hypercube Sampling (LHS) is used for the QMC sub-groups. AYLeSS is tested on 4 designs of 2 example circuits in 0.35 μm and 90nm technologies with yield from about 50% to 90%. Experimental results show that AYLeSS has approximately a 2 times speed enhancement compared with the best state-of-the-art method.