2015 · 47 citations · 9 references
Life SciencesMaterials EngineeringMaterials ScienceNeural CircuitryNeural EngineeringSynaptic PlasticityMemristive MaterialsNeural InterfacesNeuromodulationSynaptic TransmissionArtificial Synapse MaterialsElectrode DesignNeural Electrode MaterialsNeuromorphic DevicesNeuromorphic HardwareNeuromorphic Synaptic MaterialsRedox ControlSynaptic Interface Materials
We report novel nanoscale synapse and neuron devices for ultra-high density neuromorphic system. By adopting a Mo electrode, the redox reaction at Mo/Pr <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0.7</sub> Ca <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0.3</sub> MnO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sub> (PCMO) interface was controlled which in turn significantly improve synapse characteristics such as switching uniformity, disturbance, retention and multi-level data storage under identical pulse condition. Furthermore, The NbO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> based Insulator-Metal Transition (IMT) oscillator was developed for neuron application. Finally, we have experimentally confirmed the realization of pattern recognition with high accuracy using the 11k-bit Mo/PCMO synapse array and NbO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> oscillator neuron.
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Gradient-based learning applied to document recognition
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RRAM-based synapse for neuromorphic system with pattern recognition function
Sanghyuk Park, H. Kim, MYUNGYEOP CHOO et al. · 2012 · 184 citations