ACS Nano · 2020 · 79 citations · 49 references
EngineeringComputer EngineeringElectrical EngineeringMicroelectronicsNeural EngineeringSemiconductor DevicesNanoelectronicsNeural CircuitsSynaptic TransmissionAnisotropyConductanceConductivityEnergy ConsumptionHeterogeneityNeuromorphic DevicesNeuromorphic HardwareSignal Transmission
Hardware implementation of an artificial neural network requires neuromorphic devices to process information with low energy consumption and high heterogeneity. Here we demonstrate an electrolyte-gated synaptic transistor (EGT) based on a trigonal selenium (t-Se) nanosheet. Due to the intrinsic low conductivity of the Se channel, the t-Se synaptic transistor exhibits ultralow energy consumption, less than 0.1 pJ per spike. More importantly, the intrinsic low symmetry of t-Se offers a strong anisotropy along its c- and a-axis in electrical conductance with a ratio of up to 8.6. The multiterminal EGT device exhibits an anisotropic response of filtering behavior to the same external stimulus, which enables it to mimic the heterogeneous signal transmission process of the axon–multisynapse biostructure in the human brain. The proof-of-concept device in this work represents an important step to develop neuromorphic electronics for processing complex signals.
49
Nanoscale Memristor Device as Synapse in Neuromorphic Systems
Sung Hyun Jo, Ting‐Chang Chang, Idongesit E. Ebong et al. · Nano Letters · 2010 · 4.1K citations
Life Sciences, Electrical Engineering, Materials Science +17
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
Optoelectronic resistive random access memory for neuromorphic vision sensors
Feichi Zhou, Zheng Zhou, Jiewei Chen et al. · Nature Nanotechnology · 2019 · 1.3K citations