14,277 research outputs found

    Analog VLSI-Based Modeling of the Primate Oculomotor System

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    One way to understand a neurobiological system is by building a simulacrum that replicates its behavior in real time using similar constraints. Analog very large-scale integrated (VLSI) electronic circuit technology provides such an enabling technology. We here describe a neuromorphic system that is part of a long-term effort to understand the primate oculomotor system. It requires both fast sensory processing and fast motor control to interact with the world. A one-dimensional hardware model of the primate eye has been built that simulates the physical dynamics of the biological system. It is driven by two different analog VLSI chips, one mimicking cortical visual processing for target selection and tracking and another modeling brain stem circuits that drive the eye muscles. Our oculomotor plant demonstrates both smooth pursuit movements, driven by a retinal velocity error signal, and saccadic eye movements, controlled by retinal position error, and can reproduce several behavioral, stimulation, lesion, and adaptation experiments performed on primates

    Accurate a priori signal integrity estimation using a multilevel dynamic interconnect model for deep submicron VLSI design.

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    A multilevel dynamic interconnect model was derived for accurate a priori signal integrity estimates. Cross-talk and delay estimations over interconnects in deep submicron technology were analyzed systematically using this model. Good accuracy and excellent time-efficiency were found compared with electromagnetic simulations. We aim to build a dynamic interconnect library with this model to facilitate the interconnect issues for future VLSI design

    A survey of carbon nanotube interconnects for energy efficient integrated circuits

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    This article is a review of the state-of-art carbon nanotube interconnects for Silicon application with respect to the recent literature. Amongst all the research on carbon nanotube interconnects, those discussed here cover 1) challenges with current copper interconnects, 2) process & growth of carbon nanotube interconnects compatible with back-end-of-line integration, and 3) modeling and simulation for circuit-level benchmarking and performance prediction. The focus is on the evolution of carbon nanotube interconnects from the process, theoretical modeling, and experimental characterization to on-chip interconnect applications. We provide an overview of the current advancements on carbon nanotube interconnects and also regarding the prospects for designing energy efficient integrated circuits. Each selected category is presented in an accessible manner aiming to serve as a survey and informative cornerstone on carbon nanotube interconnects relevant to students and scientists belonging to a range of fields from physics, processing to circuit design

    A bibliography on parallel and vector numerical algorithms

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    This is a bibliography of numerical methods. It also includes a number of other references on machine architecture, programming language, and other topics of interest to scientific computing. Certain conference proceedings and anthologies which have been published in book form are listed also

    Tunable Integrated-Optics Nanoscaled Devices Based on Magnetic Photonic Crystals

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    Magnetooptical properties of magnetic photonic crystals have been investigated in the view of their possible applications for the modern integrated-optics devices. A "transfer matrices" formalism was expanded for the case of oblique light incidence on the periodic nanoscaled magnetic multilayered systems. Several new effects such as the Faraday effect dependence on the incidence angle and the tunability of the bandgap defect modes spectral location by external magnetic fields were found. Several possibilities of one-dimensional magnetic photonic crystals applications for the optical devices are discussed. Initial steps towards the practical implementation of the proposed devices are reported.Comment: Submitted on behalf of TIMA Editions (http://irevues.inist.fr/tima-editions

    Design Of Neural Network Circuit Inside High Speed Camera Using Analog CMOS 0.35 ¼m Technology

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    Analog VLSI on-chip learning Neural Networks represent a mature technology for a large number of applications involving industrial as well as consumer appliances. This is particularly the case when low power consumption, small size and/or very high speed are required. This approach exploits the computational features of Neural Networks, the implementation efficiency of analog VLSI circuits and the adaptation capabilities of the on-chip learning feedback schema. High-speed video cameras are powerful tools for investigating for instance the biomechanics analysis or the movements of mechanical parts in manufacturing processes. In the past years, the use of CMOS sensors instead of CCDs has enabled the development of high-speed video cameras offering digital outputs , readout flexibility, and lower manufacturing costs. In this paper, we propose a high-speed smart camera based on a CMOS sensor with embedded Analog Neural Network

    Simulation of intrinsic parameter fluctuations in decananometer and nanometer-scale MOSFETs

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    Intrinsic parameter fluctuations introduced by discreteness of charge and matter will play an increasingly important role when semiconductor devices are scaled to decananometer and nanometer dimensions in next-generation integrated circuits and systems. In this paper, we review the analytical and the numerical simulation techniques used to study and predict such intrinsic parameters fluctuations. We consider random discrete dopants, trapped charges, atomic-scale interface roughness, and line edge roughness as sources of intrinsic parameter fluctuations. The presented theoretical approach based on Green's functions is restricted to the case of random discrete charges. The numerical simulation approaches based on the drift diffusion approximation with density gradient quantum corrections covers all of the listed sources of fluctuations. The results show that the intrinsic fluctuations in conventional MOSFETs, and later in double gate architectures, will reach levels that will affect the yield and the functionality of the next generation analog and digital circuits unless appropriate changes to the design are made. The future challenges that have to be addressed in order to improve the accuracy and the predictive power of the intrinsic fluctuation simulations are also discussed
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