Titlebook: Memristors and Memristive Systems; Ronald Tetzlaff Book 2014 Springer Science+Business Media New York 2014 EmergingMemory.HP Memristors.Me

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Application of the Volterra Series Paradigm to Memristive Systemssistance opens new opportunities in IC electronics. However, considerable progress in the design of novel memristor-based circuits and systems may not be achieved unless the nonlinear dynamics of these nano-devices is fully unfolded and modeled. Due to the strongly nonlinear behavior of the physical

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Memristive Devices: Switching Effects, Modeling, and Applicationsaces significant challenges at both the fundamental and practical levels [1]. Possible solutions include .—developing new, alternative device structures, and materials while maintaining the same basic computer architecture, and .—enabling alternative computing architectures and hybrid integration to

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Silicon Nanowire-Based Memristive Devices, and sensing applications. It is shown that three- and four- terminal memristive devices can be used for both logic and memory applications. In particular, Schottky-barrier silicon nanowire FETs are very interesting devices due to their CMOS-compatibility and ease of fabrication.

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Memristor-Based Resistive Computingficient resistive logic gates and signal processing. A reconfigurable nonvolatile computing platform that harnesses memristor properties is devised to deploy massive arrays of nanoscale resistive memory devices and advance their computing capabilities with much lowered energy consumption than the co

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Memristor Device Engineering and CMOS Integration for Reconfigurable Logic Applicationsendurance, switch speed, IV nonlinearity, CMOS compatibility, ON/OFF ratio, etc.) of memristors as switches are discussed. Device engineering approaches including fabrication techniques, choice of materials, and geometry engineering are then reviewed. Finally, hybrid memristor/CMOS circuits that int

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Spike-Timing-Dependent-Plasticity in Hybrid Memristive-CMOS Spiking Neuromorphic Systemsrs. Specifically, we are linking one type of memristor nano technology devices to the biological synaptic update rule known as Spike-Time-Dependent-Plasticity found in real biological synapses. Understanding this link allows neuromorphic engineers to develop circuit architectures that use this type

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Memristor for Neuromorphic Applications: Models and Circuit Implementations in the literature. Among them the generalized Boundary Condition Memristor model sticks out for the adaptability of the dynamics at the boundaries and for the tunability of the nonvolatile behavior. The first part of the paper describes in some detail the PSpice implementation of the generalized Bo