AI Article Synopsis
- Artificial synaptic devices, designed to mimic biological synapses, are gaining attention for their potential in brain-inspired computing, particularly through memristive technology.
- A new class of these devices, called memtranstors, can continuously adjust their synaptic weights using engineered voltage pulses, enabling a variety of synaptic behaviors like long-term potentiation and depression.
- Simulations demonstrate that memtranstor networks can learn patterns effectively while consuming low energy, highlighting their promise in future computing applications.
Article 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(Mg Nb )O -0.3PbTiO /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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| http://dx.doi.org/10.1002/adma.201706717 | DOI Listing |
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