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Titolo:
Efficient and accurate crosstalk prediction via neural net-based topological decomposition of 3-D interconnect
Autore:
Ilumoka, AA;
Indirizzi:
Georgia Inst Technol, Petit Microelect Res Ctr, Atlanta, GA 30332 USA Georgia Inst Technol Atlanta GA USA 30332 Res Ctr, Atlanta, GA 30332 USA
Titolo Testata:
IEEE TRANSACTIONS ON ADVANCED PACKAGING
fascicolo: 3, volume: 24, anno: 2001,
pagine: 268 - 276
SICI:
1521-3323(200108)24:3<268:EAACPV>2.0.ZU;2-4
Fonte:
ISI
Lingua:
ENG
Soggetto:
OPTIMIZATION;
Keywords:
circuit simulation; example-based learning; interconnect delay and crosstalk prediction; interconnect modeling; neural networks;
Tipo documento:
Article
Natura:
Periodico
Settore Disciplinare:
Engineering, Computing & Technology
--discip_EC--
Citazioni:
20
Recensione:
Indirizzi per estratti:
Indirizzo: Ilumoka, AA Georgia Inst Technol, Petit Microelect Res Ctr, Atlanta, GA 30332 USA Georgia Inst Technol Atlanta GA USA 30332 lanta, GA 30332 USA
Citazione:
A.A. Ilumoka, "Efficient and accurate crosstalk prediction via neural net-based topological decomposition of 3-D interconnect", IEEE T AD P, 24(3), 2001, pp. 268-276

Abstract

Crosstalk-related issues have become increasingly important with deep submicron downscaling of ICs and wafer scale integration. In today's sytems-on-a-chip, the delay through a wire is often greater than the delay through the gate driving it. Furthermore, because of significant parasitic effects, crosstalk between signals on wires can cause major problems,, Improved management of the emi problem is made possible via EDA tools which have the capability of accurately and efficiently modeling electromagnetic interference effects in nanoscale VLSI. However, existing tools are computationally expensive and do not have broad application. The novel methodology proposed in this paper involves topological decomposition of small portions of interconnect (referred to as wirecells) at an extreme level of detail and the creation of parameterized models of these primitive interconnect structures using modular artificial neural networks (MANNs). The technique uses a finite element method program coupled with a circuit simulator and a neural networkmulti-paradigm prototyping system to produce a library of standard MANN-based wirecell models. It is especially attractive because none of the existing approaches is capable of fully modeling the simultaneous effect on delayand crosstalk of several uncorrelated variables such as interconnect length, width, thickness, separation, metal and insulating medium conductivity and relative permittivity for multiple systems of conductors. The library models derived are used to predict delay noise and crosstalk resulting from interconnect structures embedded in actual analog and digital circuitry. Contours of equi-coupling called isocouples are derived for noise characterization and used to guide design activities such as placement and routing. Matched devices for instance, can be placed on the same isocouple and regions of high delay noise and crosstalk density can be identified and eliminated through re-routing. Circuit nodes located on critical paths and having noise in excess of predefined thresholds can also be identified. The procedure is applicable to between-chip interconnect and other packaging situations. Experimental results from a combinational logic circuit and a ring oscillator demonstrate the ability of the approach to successfully predict coupled noise in very modest cpu times.

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Documento generato il 07/07/20 alle ore 11:35:32