A new algorithm to determine the number and value of realistic worst-case models for the performance of module library components is presented in this paper. The proposed algorithm employs principal components analysis (PCA) at the performance level to identify the main independent sources of variance for the performance of a set of library modules. Response surfaces methodology (RSM) and propagation of variance (POV) based algorithms are used to efficiently compute the performance level covariance matrix and nonlinear maximum likelihood optimization to trace back worst case models at the SPICE level. The effectiveness of the proposed methodology has been demonstrated by determining a realistic set of worst case models for a 0.25 μm CMOS standard cell library.
Realistic Worst-Case Modeling by Performance Level Principal Component Analysis
NEVIANI, ANDREA;
2000
Abstract
A new algorithm to determine the number and value of realistic worst-case models for the performance of module library components is presented in this paper. The proposed algorithm employs principal components analysis (PCA) at the performance level to identify the main independent sources of variance for the performance of a set of library modules. Response surfaces methodology (RSM) and propagation of variance (POV) based algorithms are used to efficiently compute the performance level covariance matrix and nonlinear maximum likelihood optimization to trace back worst case models at the SPICE level. The effectiveness of the proposed methodology has been demonstrated by determining a realistic set of worst case models for a 0.25 μm CMOS standard cell library.Pubblicazioni consigliate
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
