The Application of Regression Analysis in Modeling Interconnect Resistance

DOI

10.2991/lemcs-14.2014.156How to use a DOI?

Keywords

interconnect; resistance;Regression analysis;MOM;IC

Abstract

As clock frequencies exceed giga-Hertz, the skin depth in analog and digital circuits decreases heavily. The technology scaling have lead to complications that pose significant challenges to on-chip interconnect design, such as the distortion of signal pulses. One of the most important causes of pulse distortion is the frequency-dependence of conductor loss, which can be incorporated into circuit models for interconnects as frequency-dependent resistance per-unit-length. This makes accurate resistance necessary in simulation. In order to achieve high simulation efficiency, closed-form formulae are often used to represent frequency-dependent resistance of interconnects. In this letter, two closed-form formulae for the frequency-dependent per-unit-length resistance of rectangular cross-sectional interconnects are presented. The frequency-dependent per-unit-length resistance of a rectangular interconnect line or an interconnect line with a ground plane structure is first obtained by the method of moments (MOM). Based on this strict numerical MOM results, the novel closed-form formulae for a rectangular interconnect are fitted out using the regression analysis. The formulae can be widely used for analyzing on-chip power grid IR-drop when the frequency is changing. Compared to the previously published formulae for an interconnect, the formula provided here is more accurate during the frequency transition range.

Copyright

© 2014, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Baojun Chen
AU  - Ke Zhao
AU  - Yanjie Ju
PY  - 2014/05
DA  - 2014/05
TI  - The Application of Regression Analysis in Modeling Interconnect Resistance
BT  - Proceedings of the International Conference on Logistics, Engineering, Management and Computer Science
PB  - Atlantis Press
SP  - 684
EP  - 687
SN  - 1951-6851
UR  - https://doi.org/10.2991/lemcs-14.2014.156
DO  - 10.2991/lemcs-14.2014.156
ID  - Chen2014/05
ER  -