Worst-Case Analysis of Electrical and Electronic Equipment via Affine Arithmetic

In the design and fabrication process of electronic equipment, there are many unkown parameters which significantly affect the product performance. Some uncertainties are due to manufacturing process fluctuations, while others due to the environment such as operating temperature, voltage, and various ambient aging stressors. It is desirable to consider these uncertainties to ensure product performance, improve yield, and reduce design cost. Since direct electromagnetic compatibility measurements impact on both cost and time-to-market, there has been a growing demand for the availability of tools enabling the simulation of electrical and electronic equipment with the inclusion of the effects of system uncertainties. In this framework, the assessment of device response is no longer regarded as deterministic but as a random process. It is traditionally analyzed using the Monte Carlo or other sampling-based methods. The drawback of the above methods is large number of required samples to converge, which are time-consuming for practical applications. As an alternative, the inherent worst-case approaches such as interval analysis directly provide an estimation of the true bounds of the responses. However, such approaches might provide unnecessarily strict margins, which are very unlikely to occur. A recent technique, affine arithmetic, advances the interval based methods by means of handling correlated intervals. However, it still leads to over-conservatism due to the inability of considering probability information. The objective of this thesis is to improve the accuracy of the affine arithmetic and broaden its application in frequency-domain analysis. We first extend the existing literature results to the efficient time-domain analysis of lumped circuits considering the uncertainties. Then we provide an extension of the basic affine arithmetic to the frequency-domain simulation of circuits. Classical tools for circuit analysis are used within a modified affine framework accounting for complex algebra and uncertainty interval partitioning for the accurate and efficient computation of the worst case bounds of the responses of both lumped and distributed circuits. The performance of the proposed approach is investigated through extensive simulations in several case studies. The simulation results are compared with the Monte Carlo method in terms of both simulation time and accuracy

The Partial Elements Equivalent Circuit Method: The State Of The Art

This year marks about half a century since the birth of the technique known as the partial element equivalent circuit modeling approach. This method was initially conceived to model the behavior of interconnect-type problems for computer-integrated circuits. An important industrial requirement was the computation of general inductances in integrated circuits and packages. Since then, the advances in methods and applications made it suitable for modeling a large class of electromagnetic problems, especially in the electromagnetic compatibility (EMC)/signal and power integrity (SI/PI) areas. The purpose of this article is to present an overview of all aspects of the method, from its beginning to the present day, with special attention to the developments that have made it suitable for EMC/SI/PI problems

The 1991 3rd NASA Symposium on VLSI Design

Papers from the symposium are presented from the following sessions: (1) featured presentations 1; (2) very large scale integration (VLSI) circuit design; (3) VLSI architecture 1; (4) featured presentations 2; (5) neural networks; (6) VLSI architectures 2; (7) featured presentations 3; (8) verification 1; (9) analog design; (10) verification 2; (11) design innovations 1; (12) asynchronous design; and (13) design innovations 2

Graduate Course Descriptions, 2005 Fall

Wright State University graduate course descriptions from Fall 2005

Graduate Course Descriptions, 2006 Winter

Wright State University graduate course descriptions from Winter 2006

Digital Filters

The new technology advances provide that a great number of system signals can be easily measured with a low cost. The main problem is that usually only a fraction of the signal is useful for different purposes, for example maintenance, DVD-recorders, computers, electric/electronic circuits, econometric, optimization, etc. Digital filters are the most versatile, practical and effective methods for extracting the information necessary from the signal. They can be dynamic, so they can be automatically or manually adjusted to the external and internal conditions. Presented in this book are the most advanced digital filters including different case studies and the most relevant literature

Model order reduction techniques for circuit simulation

Includes bibliographical references (p. 156-160).Supported in part by the Semiconductor Research Corporation. SRC 93-SJ-558 Supported in part by the National Science Foundation / Advanced Research Projects Agency. MIP 91-17724Luis Miguel Silveira

Model order reduction techniques for circuit simulation

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1994.Includes bibliographical references.by ?Luís Miguel Silveira.Ph.D

Bio-inspired VLSI Systems: from Synapse to Behavior

We investigate VLSI systems using biological computational principles. The elegance of biological systems throughout the structure levels provides possible solutions to many engineering challenges. Specifically, we investigate neural systems at the synaptic level and at the sensorimotor integration level, which inspire our similar implementations in silicon. For both VLSI systems, we use floating gate MOSFETs in standard CMOS processes as nonvolatile storage elements, which enable adaptation and programmability. We propose a compact silicon stochastic synapse and methods to incorporate activity-dependent dynamics, which emulate a biological stochastic synapse. We implement and demonstrate the first silicon stochastic synapse with short-term depression by modulating the influence of noise on the circuit. The circuit exhibits true randomness and similar behavior of rate normalization and information redundancy reduction as its biological counterparts. The circuit behavior also agrees well with the theory and simulation of a circuit model based on a subtractive single release model. To understand the stochastic behavior of the silicon stochastic synapse and the stochastic operation of conventional circuits due to semiconductor technology scaling, we develop the stochastic modeling of circuits and transient analysis from the numerical solution of the stochastic model. The analytical solution of steady state distribution could be obtained from first principles. Small signal stochastic models show the interaction between noise and circuit dynamics, elucidating the effect of device parameters and biases on the stochastic behavior. We investigate optic flow wide field integration based navigation inspired from the fly in simulation, theory, and VLSI design. We generalize the framework to limited view angles. We design and test an integrated motion image sensor with on-chip optic flow estimation, adaptation, and programmable spatial filtering to directly interface with actuators for autonomous navigation. This is the first reported image sensor that uses the spatial motion pattern to extract motion parameters enabled by the mismatch compensation and programmable filters. The sensor is integrated with a ground vehicle and navigation through simple tunnel environments is demonstrated. It provides light weight and low power integrated approach to autonomous navigation of micro air vehicles