Analytical Modeling of Process Variation and Variation-Aware Parallel Circuit Simulation
Date Issued
2011
Date
2011
Author(s)
Liu, Jui-Hsiang
Abstract
With the significant advancement of statistical timing and yield analysis algorithms, there is a strong need for accurate and analytical spatial correlation models. Inflexibility of the modeling capability could induce errors during statistical modeling and in the worst
case induce large errors when the model is further used in circuit simulation. In this thesis, we first propose a novel spatial correlation modeling method not only can capture the general spatial correlation relationship, but also can generate highly accurate and analytical models.
We also propose an efficient correlation-aware non-Gaussian statistical and parallel circuit simulation algorithm to combine with the proposed spatial correlation model. Our proposed method first partitions the traditional linear system into several sub-blocks. Then, multi-threading technique is applied to each sub-block simultaneously. Moreover, the efficient out-of-core scheme is proposed to dramatically reduce the memory effort without losing performance. The memory usage of our algorithm can be reduced to 29%.
Instead of using expensive Monte Carlo simulation, we simultaneously solve the mean and variance in ordinary equations with quadratic Gaussian random variables. Our experimental results show that our proposed approach can achieve over 700X speed up over fundamental Monte Carlo simulation with just negligible errors.
Subjects
VLSI
Process Variation
Statistical Modeling
Circuit Simulation
Parallel Computing
Type
thesis
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