Closely following the rapid development of artificial intelligence, studies of the human brain and neurobiology are focusing on the biological mechanisms of neurons and synapses. Herein, a memory system employing a nanoporous double-layer structure for simulation of synaptic functions is described. The sponge-like double-layer porous (SLDLP) oxide stack of Pt/porous LiCoO2/porous SiO2/Si is designed as presynaptic and postsynaptic membranes. This bionic structure exhibits high ON–OFF ratios up to 108 during the stability test, and data can be maintained for 105 s despite a small read voltage of 0.5 V. Typical synaptic functions, such as nonlinear transmission characteristics, spike-timing-dependent plasticity, and learning-experience behaviors, are achieved simultaneously with this device. Based on the hydrodynamic transport mechanism of water molecules in porous sponges and the principle of water storage, the synaptic behavior of the device is discussed. The SLDLP oxide memristor is very promising due to its excellent synaptic performance and potential in neuromorphic computing.
A memristor
architecture based on porous oxide materials has the
potential to be used in artificial synaptic devices. Herein, we present
a memristor system employing a karst-like hierarchically porous (KLHP)
silicon oxide structure with good stability and repeatability. The
KLHP structure prepared by an electrochemical process and thermal
oxidation exhibits high ON-OFF ratios up to 105 during
the endurance test, and the data can be maintained for 105 s at a small read voltage 0.1 V. The mechanism of lithium ion migration
in the porous silicon oxide structure has been discussed by a simulated
model. The porous silicon oxide-based memristor is very promising
because of the enhanced performance as well as easily accessed neuromorphic
computing.
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