2018
DOI: 10.1007/978-3-319-75465-9_4
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Gate Delay Under Process Variations

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“…If it is not practical to calculate all N S values of 𝐴𝐴  𝑖𝑖 , due to computational expense, a subset N U of the overall set of post burn-in models may be used (see Section 4). The relationship between the uncertainty on a physical prediction, 𝐴𝐴 V𝑉 (𝑓𝑓 (ξ‰ž )) , and the uncertainty on the underlying model parameters, 𝐴𝐴 V𝑽 (ξ‰ž ) , is dependent on the sensitivity of 𝐴𝐴 𝐴𝐴(ξ‰ž ) to each parameter, 𝐴𝐴 𝑖𝑖 (i.e., the gradient, 𝐴𝐴 𝐴𝐴𝐴𝐴 (ξ‰ž )βˆ•π΄π΄ξ‰„π‘–π‘– ), and the covariance structure of the model, 𝐴𝐴 𝚺𝚺 ξ‰ž (Champac & Garcia Gervacio, 2018). In the case of the anelasticity parameterization, 𝐴𝐴 𝐴𝐴(ξ‰ž ) and 𝐴𝐴 𝐴𝐴(ξ‰ž ) are non-linear functions of V S , complicating the analytical calculation of their expectation value and variance.…”
Section: Bayesian Modeling Frameworkmentioning
confidence: 99%
“…If it is not practical to calculate all N S values of 𝐴𝐴  𝑖𝑖 , due to computational expense, a subset N U of the overall set of post burn-in models may be used (see Section 4). The relationship between the uncertainty on a physical prediction, 𝐴𝐴 V𝑉 (𝑓𝑓 (ξ‰ž )) , and the uncertainty on the underlying model parameters, 𝐴𝐴 V𝑽 (ξ‰ž ) , is dependent on the sensitivity of 𝐴𝐴 𝐴𝐴(ξ‰ž ) to each parameter, 𝐴𝐴 𝑖𝑖 (i.e., the gradient, 𝐴𝐴 𝐴𝐴𝐴𝐴 (ξ‰ž )βˆ•π΄π΄ξ‰„π‘–π‘– ), and the covariance structure of the model, 𝐴𝐴 𝚺𝚺 ξ‰ž (Champac & Garcia Gervacio, 2018). In the case of the anelasticity parameterization, 𝐴𝐴 𝐴𝐴(ξ‰ž ) and 𝐴𝐴 𝐴𝐴(ξ‰ž ) are non-linear functions of V S , complicating the analytical calculation of their expectation value and variance.…”
Section: Bayesian Modeling Frameworkmentioning
confidence: 99%