TY - GEN
T1 - Efficient VCO phase macromodel generation considering statistical parametric variations
AU - Dong, Wei
AU - Feng, Zhuo
AU - Li, Peng
PY - 2007
Y1 - 2007
N2 - 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 signi cantly 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 signi cantly 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.
AB - 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 signi cantly 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 signi cantly 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.
UR - http://www.scopus.com/inward/record.url?scp=50249174037&partnerID=8YFLogxK
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U2 - 10.1109/ICCAD.2007.4397374
DO - 10.1109/ICCAD.2007.4397374
M3 - Conference contribution
AN - SCOPUS:50249174037
SN - 1424413826
SN - 9781424413829
T3 - IEEE/ACM International Conference on Computer-Aided Design, Digest of Technical Papers, ICCAD
SP - 874
EP - 878
BT - 2007 IEEE/ACM International Conference on Computer-Aided Design, ICCAD
T2 - 2007 IEEE/ACM International Conference on Computer-Aided Design, ICCAD
Y2 - 4 November 2007 through 8 November 2007
ER -