Custom Integrated Circuits

Contains table of contents for Part III, table of contents for Section 1 and reports on eleven research projects.IBM CorporationMIT School of EngineeringNational Science Foundation Grant MIP 94-23221Defense Advanced Research Projects Agency/U.S. Army Intelligence Center Contract DABT63-94-C-0053Mitsubishi CorporationNational Science Foundation Young Investigator Award Fellowship MIP 92-58376Joint Industry Program on Offshore Structure AnalysisAnalog DevicesDefense Advanced Research Projects AgencyCadence Design SystemsMAFET ConsortiumConsortium for Superconducting ElectronicsNational Defense Science and Engineering Graduate FellowshipDigital Equipment CorporationMIT Lincoln LaboratorySemiconductor Research CorporationMultiuniversity Research IntiativeNational Science Foundatio

Comparaison des méthodes de réduction d'ordre POD et Krylov, application à la méthode PEEC

International audienceDe nos jours, les logiciels de simulation physique ont la capacité de produire des modèles très précis, mais en contrepartie, ils sont très coûteux en temps de calcul et en mémoire. Ce travail présente la comparaison de deux méthodes de réduction d'ordre : la décomposition orthogonale aux valeurs propres (POD) et la méthode de correspondance des moments (Moment Matching / Krylov). Une façon automatique de choisir les points d'expansion est proposée. La mesure d'impédance d'une antenne est utilisée comme exemple d'application. Les avantages et désavantages de chaque méthode sont présentés et discutés

An Arnoldi Approach for Generation of Reduced Order Models for Turbomachinery

A linear reduced-order aerodynamic model is developed for aeroelastic analysis of turbo-machines. The basis vectors are constructed using a block Arnoldi method. Although the model is cast in the time domain in state-space form, the spatial periodicity of the problem is exploited in the frequency domain to obtain these vectors efficiently. The frequency domain proper orthogonal decomposition is identified as a special case of the Arnoldi method. The aerodynamic model is coupled with a simple structural model that has two degrees of freedom for each blade. The technique is applicable to viscous and three-dimensional problems as well as multi-stage problems with inlet and exit disturbance flows, although here results are presented for two-dimensional, inviscid flow through a twenty-blade single-stage rotor. In this case, the number of states of the model is on the order of ten per blade passage, making it appropriate for control applications

Optimization techniques for high-performance digital circuits

The relentless push for high performance in custom dig-ital circuits has led to renewed emphasis on circuit opti-mization or tuning. The parameters of the optimization are typically transistor and interconnect sizes. The de-sign metrics are not just delay, transition times, power and area, but also signal integrity and manufacturability. This tutorial paper discusses some of the recently pro-posed methods of circuit optimization, with an emphasis on practical application and methodology impact. Circuit optimization techniques fall into three broad categories. The rst is dynamic tuning, based on time-domain simulation of the underlying circuit, typically combined with adjoint sensitivity computation. These methods are accurate but require the specication of in-put signals, and are best applied to small data- ow cir-cuits and \cross-sections " of larger circuits. Ecient sensitivity computation renders feasible the tuning of cir-cuits with a few thousand transistors. Second, static tuners employ static timing analysis to evaluate the per-formance of the circuit. All paths through the logic are simultaneously tuned, and no input vectors are required. Large control macros are best tuned by these methods. However, in the context of deep submicron custom de-sign, the inaccuracy of the delay models employed by these methods often limits their utility. Aggressive dy-namic or static tuning can push a circuit into a precip-itous corner of the manufacturing process space, which is a problem addressed by the third class of circuit op-timization tools, statistical tuners. Statistical techniques are used to enhance manufacturability or maximize yield. In addition to surveying the above techniques, topics such as the use of state-of-the-art nonlinear optimization methods and special considerations for interconnect siz-ing, clock tree optimization and noise-aware tuning will be brie y considered.

An adaptive-order rational Arnoldi method for model-order reductions of linear time-invariant systems

AbstractThis work proposes a model reduction method, the adaptive-order rational Arnoldi (AORA) method, to be applied to large-scale linear systems. It is based on an extension of the classical multi-point Padé approximation (or the so-called multi-point moment matching), using the rational Arnoldi iteration approach. Given a set of predetermined expansion points, an exact expression for the error between the output moment of the original system and that of the reduced-order system, related to each expansion point, is derived first. In each iteration of the proposed adaptive-order rational Arnoldi algorithm, the expansion frequency corresponding to the maximum output moment error will be chosen. Hence, the corresponding reduced-order model yields the greatest improvement in output moments among all reduced-order models of the same order. A detailed theoretical study is described. The proposed method is very appropriate for large-scale electronic systems, including VLSI interconnect models and digital filter designs. Several examples are considered to demonstrate the effectiveness and efficiency of the proposed method

A Coordinate-Transformed Arnoldi Algorithm for Generating Guaranteed Stable Reduced-Order Models of RLC Circuits.

Since the first papers on asymptotic waveform evaluation (AWE), Pade-based reduced order models have become standard for improving coupled circuit-interconnect simulation efficiency. Such models can be accurately computed using bi-orthogonalization algorithms like Pad e via Lanczos (PVL), but the resulting Pad e approximates can still be unstable even when generated from stable RLC circuits. For certain classes of RC circuits it has been shown that congruence transforms, like the Arnoldi algorithm, can generate guaranteed stable and passive reduced-order models. In this paper we present a computationally efficient model-order reduction technique, the coordinate-transformed Arnoldi algorithm, and show that this method generates arbitrarily accurate and guaranteed stable reduced-order models for RLC circuits. Examples are presented which demonstrates the enhanced stability and efficiency of the new method