Reducing the Complexity of VLSI Performance Variation Modeling Via Parameter Dimension Reduction

Feng, Zhuo; Guoyu,

doi:10.1109/isqed.2007.136

Cited by 4 publications

(2 citation statements)

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“…We briefly review the parameter reduction technique in [7], [11] and extend it to a more robust iterative scheme, which combines the linear and quadratic regression models.…”

Section: Parameter Reduction Techniquementioning

confidence: 99%

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Efficient VCO phase macromodel generation considering statistical parametric variations

Wang¹,

Zhang²

2007

2007 IEEE/ACM International Conference on Computer-Aided Design

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With the growing concern of process variability, parameterized circuit models are becoming increasingly important for circuit design and veri cation. Although techniques exist to extract compact VCO phase macromodels, a direct parametrization of VCO macromodels over a large set of parametric variations not only results in highly complex models, but also leads to signi cantly high computational cost. In this paper, an ef cient parameterized VCO phase model generation technique is presented to capture the impacts of statistical parametric variations. The model extraction cost of our approach is signi cantly reduced by exploiting circuit-speci c parameter dimension reduction, which effectively reduces the parameter space dimension over which the phase model needs to be extracted. The application of parameter reduction is facilitated by a novel and fast time-domain sampling technique that provides the essential statistical correlation data. Our numerical experiments have shown that the proposed model generation approach is more ef cient than brute-force parametric modeling while producing accurate parameterized phase models that can capture large range parametric variations.

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“…We briefly review the parameter reduction technique in [7], [11] and extend it to a more robust iterative scheme, which combines the linear and quadratic regression models.…”

Section: Parameter Reduction Techniquementioning

confidence: 99%

“…αt c (p1, p2, · · · , pN ) = t c f ree − tc (11) Considering N zero-crossing points, this gives a system of N nonlinear equations in terms of N unknowns.…”

Section: Model Generationmentioning

confidence: 99%