IEICE TRANSACTIONS on Fundamentals
Extracting Device-Parameter Variations with RO-Based Sensors
Summary :
Device-parameter estimation sensors inside a chip are gaining its importance as the post-fabrication tuning is becoming of a practical use. In estimation of variational parameters using on-chip sensors, it is often assumed that the outputs of variation sensors are not affected by random variations. However, random variations can deteriorate the accuracy of the estimation result. In this paper, we propose a device-parameter estimation method with on-chip variation sensors explicitly considering random variability. The proposed method derives the global variation parameters and the standard deviation of the random variability using the maximum likelihood estimation. We experimentally verified that the proposed method improves the accuracy of device-parameter estimation by 11.1 to 73.4% compared to the conventional method that neglects random variations.
- Publication
- IEICE TRANSACTIONS on Fundamentals Vol.E94-A No.12 pp.2537-2544
- Publication Date
- 2011/12/01
- Publicized
- Online ISSN
- 1745-1337
- DOI
- 10.1587/transfun.E94.A.2537
- Type of Manuscript
- Special Section PAPER (Special Section on VLSI Design and CAD Algorithms)
- Category
- Device and Circuit Modeling and Analysis
Authors
Keyword
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Ken-ichi SHINKAI, Masanori HASHIMOTO, Takao ONOYE, "Extracting Device-Parameter Variations with RO-Based Sensors" in IEICE TRANSACTIONS on Fundamentals,
vol. E94-A, no. 12, pp. 2537-2544, December 2011, doi: 10.1587/transfun.E94.A.2537.
Abstract: Device-parameter estimation sensors inside a chip are gaining its importance as the post-fabrication tuning is becoming of a practical use. In estimation of variational parameters using on-chip sensors, it is often assumed that the outputs of variation sensors are not affected by random variations. However, random variations can deteriorate the accuracy of the estimation result. In this paper, we propose a device-parameter estimation method with on-chip variation sensors explicitly considering random variability. The proposed method derives the global variation parameters and the standard deviation of the random variability using the maximum likelihood estimation. We experimentally verified that the proposed method improves the accuracy of device-parameter estimation by 11.1 to 73.4% compared to the conventional method that neglects random variations.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/transfun.E94.A.2537/_p
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@ARTICLE{e94-a_12_2537,
author={Ken-ichi SHINKAI, Masanori HASHIMOTO, Takao ONOYE, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Extracting Device-Parameter Variations with RO-Based Sensors},
year={2011},
volume={E94-A},
number={12},
pages={2537-2544},
abstract={Device-parameter estimation sensors inside a chip are gaining its importance as the post-fabrication tuning is becoming of a practical use. In estimation of variational parameters using on-chip sensors, it is often assumed that the outputs of variation sensors are not affected by random variations. However, random variations can deteriorate the accuracy of the estimation result. In this paper, we propose a device-parameter estimation method with on-chip variation sensors explicitly considering random variability. The proposed method derives the global variation parameters and the standard deviation of the random variability using the maximum likelihood estimation. We experimentally verified that the proposed method improves the accuracy of device-parameter estimation by 11.1 to 73.4% compared to the conventional method that neglects random variations.},
keywords={},
doi={10.1587/transfun.E94.A.2537},
ISSN={1745-1337},
month={December},}
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TY - JOUR
TI - Extracting Device-Parameter Variations with RO-Based Sensors
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 2537
EP - 2544
AU - Ken-ichi SHINKAI
AU - Masanori HASHIMOTO
AU - Takao ONOYE
PY - 2011
DO - 10.1587/transfun.E94.A.2537
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E94-A
IS - 12
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - December 2011
AB - Device-parameter estimation sensors inside a chip are gaining its importance as the post-fabrication tuning is becoming of a practical use. In estimation of variational parameters using on-chip sensors, it is often assumed that the outputs of variation sensors are not affected by random variations. However, random variations can deteriorate the accuracy of the estimation result. In this paper, we propose a device-parameter estimation method with on-chip variation sensors explicitly considering random variability. The proposed method derives the global variation parameters and the standard deviation of the random variability using the maximum likelihood estimation. We experimentally verified that the proposed method improves the accuracy of device-parameter estimation by 11.1 to 73.4% compared to the conventional method that neglects random variations.
ER -
English
