IEEE Electron Device Letters · 2018 · 52 citations · 20 references
EngineeringElectrical EngineeringMaterials EngineeringAnalog ElectronicsNeural EngineeringSemiconductor DevicesMemristive MaterialsSpiking Neural NetworksAnalog Switching BehaviorDevice ResistanceLong-term Data RetentionMemristive CircuitsMemristive MemoryNeuromorphic DevicesNeuromorphic Synaptic Materials
Electronic synapse with precise analog weight tuning ability and long-term retention is the vital device foundation of memristor-based neuromorphic computing systems. In this letter, we propose a Ti/AlO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><i>x</i></sub> /TaO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><i>x</i></sub> /Pt memristor as an analog synapse for memristive neural network applications. The device shows high uniformity, excellent analog switching behaviors (up to 200 resistance states under triangle pulses) and excellent long-term retention of each state (up to 30 000 s). Furthermore, the precise modulation of the device resistance state (with 1.7% tolerance) can also be achieved by a finer writing program within 50 cycles.
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Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing
Zhongrui Wang, Saumil Joshi, Sergey Savel’ev et al. · Nature Materials · 2016 · 2.3K citations · Full text
Short-term plasticity and long-term potentiation mimicked in single inorganic synapses
Takeo Ohno, Tsuyoshi Hasegawa, Tohru Tsuruoka et al. · Nature Materials · 2011 · 1.8K citations
Analogue signal and image processing with large memristor crossbars
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Engineering, Computer Engineering, Electrical Engineering +16