Mimicking Synaptic Plasticity and Neural Network Using Memtranstors |
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| Authors: | Jian‐Xin Shen Da‐Shan Shang Yi‐Sheng Chai Shou‐Guo Wang Bao‐Gen Shen Young Sun |
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| Affiliation: | 1. Beijing National Laboratory for Condensed Matter Physics, Institute of Physics, Chinese Academy of Sciences, Beijing, P. R. China;2. School of Physical Science, University of Chinese Academy of Sciences, Beijing, China;3. Institute of Advanced Materials, Beijing Normal University, Beijing, China |
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| Abstract: | Artificial synaptic devices that mimic the functions of biological synapses have drawn enormous interest because of their potential in developing brain‐inspired computing. Current studies are focusing on memristive devices in which the change of the conductance state is used to emulate synaptic behaviors. Here, a new type of artificial synaptic devices based on the memtranstor is demonstrated, which is a fundamental circuit memelement in addition to the memristor, memcapacitor, and meminductor. The state of transtance (presented by the magnetoelectric voltage) in memtranstors acting as the synaptic weight can be tuned continuously with a large number of nonvolatile levels by engineering the applied voltage pulses. Synaptic behaviors including the long‐term potentiation, long‐term depression, and spiking‐time‐dependent plasticity are implemented in memtranstors made of Ni/0.7Pb(Mg1/3Nb2/3)O3‐0.3PbTiO3/Ni multiferroic heterostructures. Simulations reveal the capability of pattern learning in a memtranstor network. The work elucidates the promise of memtranstors as artificial synaptic devices with low energy consumption. |
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| Keywords: | magnetoelectric coupling memtranstors multilevel switching synaptic devices synaptic plasticity |
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