Mimicking Synaptic Plasticity and Neural Network Using Memtranstors

5 years ago

Mimicking Synaptic Plasticity and Neural Network Using Memtranstors

Shou-Guo Wang, Bao-Gen Shen, Young Sun, Yi-Sheng Chai, Da-Shan Shang, Jian-Xin Shen

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. An artifical synaptic device employing magnetoelectric effects is demonstrated based on memtranstors made of Ni/0.7Pb(Mg1/3Nb2/3)O3–0.3PbTiO3/Ni multiferroic heterostructures. The memtranstance presented by the magnetoelectric voltage serves as the synaptic weight and is tuned with a large number of nonvolatile levels to mimic the functionality of biological synapses. These results reveal the great potential of memtranstors as artificial synaptic devices with low energy consumption.

Publisher URL: http://onlinelibrary.wiley.com/resolve/doi

DOI: 10.1002/adma.201706717