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      Resistive switching memories based on metal oxides: mechanisms, reliability and scaling

      Semiconductor Science and Technology
      IOP Publishing

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          Nanoscale memristor device as synapse in neuromorphic systems.

          A memristor is a two-terminal electronic device whose conductance can be precisely modulated by charge or flux through it. Here we experimentally demonstrate a nanoscale silicon-based memristor device and show that a hybrid system composed of complementary metal-oxide semiconductor neurons and memristor synapses can support important synaptic functions such as spike timing dependent plasticity. Using memristors as synapses in neuromorphic circuits can potentially offer both high connectivity and high density required for efficient computing.
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            Phase-change materials for rewriteable data storage.

            Phase-change materials are some of the most promising materials for data-storage applications. They are already used in rewriteable optical data storage and offer great potential as an emerging non-volatile electronic memory. This review looks at the unique property combination that characterizes phase-change materials. The crystalline state often shows an octahedral-like atomic arrangement, frequently accompanied by pronounced lattice distortions and huge vacancy concentrations. This can be attributed to the chemical bonding in phase-change alloys, which is promoted by p-orbitals. From this insight, phase-change alloys with desired properties can be designed. This is demonstrated for the optical properties of phase-change alloys, in particular the contrast between the amorphous and crystalline states. The origin of the fast crystallization kinetics is also discussed.
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              Resistive switching in transition metal oxides

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                Author and article information

                Journal
                Semiconductor Science and Technology
                Semicond. Sci. Technol.
                IOP Publishing
                0268-1242
                1361-6641
                June 01 2016
                June 01 2016
                : 31
                : 6
                : 063002
                Article
                10.1088/0268-1242/31/6/063002
                746f80e5-a7a3-4d93-8c4d-c24b3862385d
                © 2016

                http://iopscience.iop.org/info/page/text-and-data-mining

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