The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Apabila kita berskala ke arah teknologi nanometer, peningkatan dalam variasi parameter antara sambungan akan membawa kebolehubahan prestasi yang ketara. Metodologi reka bentuk baharu akan muncul untuk memudahkan pembinaan sistem yang boleh dipercayai daripada komponen skala nanometer yang tidak boleh dipercayai. Metodologi sedemikian memerlukan model prestasi baharu yang menangkap realiti pembuatan dengan tepat. Dalam makalah ini, kami membentangkan model berasaskan Transformasi Pecahan Linear (LFT) untuk ketidakpastian parametrik antara sambungan. Model baharu merumuskan ketidakpastian parametrik antara sambungan sebagai struktur ketidakpastian skalar berulang. Dengan bantuan Realisasi Pemangkasan Seimbang (BTR) umum dan Ketaksamaan Matriks Linear (LMI), model yang dicadangkan mengurangkan susunan rangkaian antara sambungan asal sambil mengekalkan kestabilan. Model baharu berasaskan LFT malah menjamin pasif jika pengurangan BTR adalah berdasarkan penyelesaian kepada sepasang Ketaksamaan Matriks Linear (LMI) yang dijana daripada persamaan Lur'e. Dalam kes sejumlah besar parameter tidak pasti, model baharu boleh digunakan berturut-turut: parameter tidak pasti dibahagikan kepada kumpulan, dan berkenaan dengan setiap kumpulan, model berasaskan LFT digunakan secara bergilir-gilir.
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Salinan
Omar HAFIZ, Alexander MITEV, Janet Meiling WANG, "A Linear Fractional Transform (LFT) Based Model for Interconnect Uncertainty" in IEICE TRANSACTIONS on Fundamentals,
vol. E92-A, no. 4, pp. 1148-1160, April 2009, doi: 10.1587/transfun.E92.A.1148.
Abstract: As we scale toward nanometer technologies, the increase in interconnect parameter variations will bring significant performance variability. New design methodologies will emerge to facilitate construction of reliable systems from unreliable nanometer scale components. Such methodologies require new performance models which accurately capture the manufacturing realities. In this paper, we present a Linear Fractional Transform (LFT) based model for interconnect parametric uncertainty. The new model formulates the interconnect parametric uncertainty as a repeated scalar uncertainty structure. With the help of generalized Balanced Truncation Realization (BTR) and Linear Matrix Inequalities (LMI's), the porposed model reduces the order of the original interconnect network while preserves the stability. The LFT based new model even guarantees passivity if the BTR reduction is based on solutions to a pair of Linear Matrix Inequalities (LMI's) generated from Lur'e equations. In case of large number of uncertain parameters, the new model may be applied successively: the uncertain parameters are partitioned into groups, and with regard to each group, LFT based model is applied in turns.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/transfun.E92.A.1148/_p
Salinan
@ARTICLE{e92-a_4_1148,
author={Omar HAFIZ, Alexander MITEV, Janet Meiling WANG, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={A Linear Fractional Transform (LFT) Based Model for Interconnect Uncertainty},
year={2009},
volume={E92-A},
number={4},
pages={1148-1160},
abstract={As we scale toward nanometer technologies, the increase in interconnect parameter variations will bring significant performance variability. New design methodologies will emerge to facilitate construction of reliable systems from unreliable nanometer scale components. Such methodologies require new performance models which accurately capture the manufacturing realities. In this paper, we present a Linear Fractional Transform (LFT) based model for interconnect parametric uncertainty. The new model formulates the interconnect parametric uncertainty as a repeated scalar uncertainty structure. With the help of generalized Balanced Truncation Realization (BTR) and Linear Matrix Inequalities (LMI's), the porposed model reduces the order of the original interconnect network while preserves the stability. The LFT based new model even guarantees passivity if the BTR reduction is based on solutions to a pair of Linear Matrix Inequalities (LMI's) generated from Lur'e equations. In case of large number of uncertain parameters, the new model may be applied successively: the uncertain parameters are partitioned into groups, and with regard to each group, LFT based model is applied in turns.},
keywords={},
doi={10.1587/transfun.E92.A.1148},
ISSN={1745-1337},
month={April},}
Salinan
TY - JOUR
TI - A Linear Fractional Transform (LFT) Based Model for Interconnect Uncertainty
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 1148
EP - 1160
AU - Omar HAFIZ
AU - Alexander MITEV
AU - Janet Meiling WANG
PY - 2009
DO - 10.1587/transfun.E92.A.1148
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E92-A
IS - 4
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - April 2009
AB - As we scale toward nanometer technologies, the increase in interconnect parameter variations will bring significant performance variability. New design methodologies will emerge to facilitate construction of reliable systems from unreliable nanometer scale components. Such methodologies require new performance models which accurately capture the manufacturing realities. In this paper, we present a Linear Fractional Transform (LFT) based model for interconnect parametric uncertainty. The new model formulates the interconnect parametric uncertainty as a repeated scalar uncertainty structure. With the help of generalized Balanced Truncation Realization (BTR) and Linear Matrix Inequalities (LMI's), the porposed model reduces the order of the original interconnect network while preserves the stability. The LFT based new model even guarantees passivity if the BTR reduction is based on solutions to a pair of Linear Matrix Inequalities (LMI's) generated from Lur'e equations. In case of large number of uncertain parameters, the new model may be applied successively: the uncertain parameters are partitioned into groups, and with regard to each group, LFT based model is applied in turns.
ER -