Search for dissertations about: "thesis on memory switching"
Showing result 1 - 5 of 57 swedish dissertations containing the words thesis on memory switching.
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1. Memory Effects on Iron Oxide Filled Carbon Nanotubes
Abstract : In this Licentiate Thesis, the properties and effects of iron and iron oxide filled carbon nanotube (Fe-CNT) memories are investigated using experimental characterization and quantum physical theoretical models. Memory devices based on the simple assembly of Fe-CNTs between two metallic contacts are presented as a possible application involving the resistive switching phenomena of this material. READ MORE
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2. Charge transport modulation in organic electronic diodes
Abstract : Since the discovery of conducting polymers three decades ago the field of organic electronics has evolved rapidly. Organic light emitting diodes have already reached the consumer market, while organic solar cells and transistors are rapidly maturing. One of the great benefits with this class of materials is that they can be processed from solution. READ MORE
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3. On Hardware Implementation of Discrete-Time Cellular Neural Networks
Abstract : Cellular Neural Networks are characterized by simplicity of operation. The network consists of a large number of nonlinear processing units; called cells; that are equally spread in the space. READ MORE
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4. Multistability, Ionic Doping, and Charge Dynamics in Electrosynthesized Polypyrrole, Polymer-Nanoparticle Blend Nonvolatile Memory, and Fixed p-i-n Junction Polymer Light-Emitting Electrochemical Cells
Abstract : A variety of factors make semiconducting polymers a fascinating alternative for both device development and new areas of fundamental research. Among these are solution processability, low cost, flexibility, and the strong dependence of conduction on the presence of charge compensating ions. READ MORE
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5. Vertical III-V Nanowires For In-Memory Computing
Abstract : In recent times, deep neural networks (DNNs) have demonstrated great potential in various machine learning applications,such as image classification and object detection for autonomous driving. However, increasing the accuracy of DNNsrequires scaled, faster, and more energy-efficient hardware, which is limited by the von Neumann architecture whereseparate memory and computing units lead to a bottleneck in performance. READ MORE