Proceedings of the 2006 IEEE/ACM international conference on Computer-aided design
Proceedings of the 2006 IEEE/ACM international conference on Computer-aided design
Reducing the Complexity of VLSI Performance Variation Modeling Via Parameter Dimension Reduction
ISQED '07 Proceedings of the 8th International Symposium on Quality Electronic Design
Proceedings of the 44th annual Design Automation Conference
A methodology for timing model characterization for statistical static timing analysis
Proceedings of the 2007 IEEE/ACM international conference on Computer-aided design
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Yield-aware hierarchical optimization of large analog integrated circuits
Proceedings of the 2008 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 verification. 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 significantly high computational cost. In this paper, an efficient parameterized VCO phase model generation technique is presented to capture the impacts of statistical parametric variations. The model extraction cost of our approach is significantly reduced by exploiting circuit-specific 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 efficient than brute-force parametric modeling while producing accurate parameterized phase models that can capture large range parametric variations.