A Method for Linking Process-Level Variability to System Performances

Tomohiro FUJITA; Hidetoshi ONODERA

A Method for Linking Process-Level Variability to System Performances

Tomohiro FUJITA, Hidetoshi ONODERA

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In this paper we present a case study of a hierarchical statistical analysis. The method which we use here bridges the statistical information between process-level and system-level, and enables us to know the effect of the process variation on the system performance. We use two modeling techniques--intermediate model and response surface model--in order to link the statistical information between adjacent design levels. We show an experiment of the hierarchical statistical analysis applied to a Phase Locked Loop (PLL) circuit, and indicate that the hierarchical statistical analysis is practical with respect to both accuracy and simulation cost. Following three applications are also presented in order to show advantage of this linking method; these are Monte Carlo analysis, worst-case analysis, and sensitive analysis. The results of the Monte Carlo and the worst-case analysis indicate that this method is realistic statistical one. The result of the sensitive analysis enables us to evaluate the effect of process variation at the system level. Also, we can derive constraints on the process variation from a performance requirement.

Publication: IEICE TRANSACTIONS on Fundamentals Vol.E83-A No.12 pp.2592-2599

Publication Date: 2000/12/25

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Type of Manuscript: Special Section PAPER (Special Section on VLSI Design and CAD Algorithms)

Category: Simulation

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Tomohiro FUJITA, Hidetoshi ONODERA, "A Method for Linking Process-Level Variability to System Performances" in IEICE TRANSACTIONS on Fundamentals, vol. E83-A, no. 12, pp. 2592-2599, December 2000, doi: .
Abstract: In this paper we present a case study of a hierarchical statistical analysis. The method which we use here bridges the statistical information between process-level and system-level, and enables us to know the effect of the process variation on the system performance. We use two modeling techniques--intermediate model and response surface model--in order to link the statistical information between adjacent design levels. We show an experiment of the hierarchical statistical analysis applied to a Phase Locked Loop (PLL) circuit, and indicate that the hierarchical statistical analysis is practical with respect to both accuracy and simulation cost. Following three applications are also presented in order to show advantage of this linking method; these are Monte Carlo analysis, worst-case analysis, and sensitive analysis. The results of the Monte Carlo and the worst-case analysis indicate that this method is realistic statistical one. The result of the sensitive analysis enables us to evaluate the effect of process variation at the system level. Also, we can derive constraints on the process variation from a performance requirement.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/e83-a_12_2592/_p

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@ARTICLE{e83-a_12_2592,
author={Tomohiro FUJITA, Hidetoshi ONODERA, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={A Method for Linking Process-Level Variability to System Performances},
year={2000},
volume={E83-A},
number={12},
pages={2592-2599},
abstract={In this paper we present a case study of a hierarchical statistical analysis. The method which we use here bridges the statistical information between process-level and system-level, and enables us to know the effect of the process variation on the system performance. We use two modeling techniques--intermediate model and response surface model--in order to link the statistical information between adjacent design levels. We show an experiment of the hierarchical statistical analysis applied to a Phase Locked Loop (PLL) circuit, and indicate that the hierarchical statistical analysis is practical with respect to both accuracy and simulation cost. Following three applications are also presented in order to show advantage of this linking method; these are Monte Carlo analysis, worst-case analysis, and sensitive analysis. The results of the Monte Carlo and the worst-case analysis indicate that this method is realistic statistical one. The result of the sensitive analysis enables us to evaluate the effect of process variation at the system level. Also, we can derive constraints on the process variation from a performance requirement.},
keywords={},
doi={},
ISSN={},
month={December},}

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TY - JOUR
TI - A Method for Linking Process-Level Variability to System Performances
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 2592
EP - 2599
AU - Tomohiro FUJITA
AU - Hidetoshi ONODERA
PY - 2000
DO -
JO - IEICE TRANSACTIONS on Fundamentals
SN -
VL - E83-A
IS - 12
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - December 2000
AB - In this paper we present a case study of a hierarchical statistical analysis. The method which we use here bridges the statistical information between process-level and system-level, and enables us to know the effect of the process variation on the system performance. We use two modeling techniques--intermediate model and response surface model--in order to link the statistical information between adjacent design levels. We show an experiment of the hierarchical statistical analysis applied to a Phase Locked Loop (PLL) circuit, and indicate that the hierarchical statistical analysis is practical with respect to both accuracy and simulation cost. Following three applications are also presented in order to show advantage of this linking method; these are Monte Carlo analysis, worst-case analysis, and sensitive analysis. The results of the Monte Carlo and the worst-case analysis indicate that this method is realistic statistical one. The result of the sensitive analysis enables us to evaluate the effect of process variation at the system level. Also, we can derive constraints on the process variation from a performance requirement.
ER -