Synaptic Learning and Memory Functions Achieved Using Oxygen Ion Migration/Diffusion in an Amorphous InGaZnO Memristor

Abstract

A single synaptic device with inherent learning and memory functions is demonstrated based on an amorphous InGaZnO (α‐IGZO) memristor; several essential synaptic functions are simultaneously achieved in such a single device, including nonlinear transmission characteristics, spike‐rate‐dependent and spike‐timing‐dependent plasticity, long‐term/short‐term plasticity (LSP and STP) and “learning‐experience” behavior. These characteristics bear striking resemblances to certain learning and memory functions of biological systems. Especially, a “learning‐experience” function is obtained for the first time, which is thought to be related to the metastable local structures in α‐IGZO. These functions are interrelated: frequent stimulation can cause an enhancement of LTP, both spike‐rate‐dependent and spike‐timing‐dependent plasticity is the same on this point; and, the STP‐to‐LTP transition can occur through repeated “stimulation” training. The physical mechanism of device operation, which does not strictly follow the memristor model, is attributed to oxygen ion migration/diffusion. A correlation between short‐term memory and ion diffusion is established by studying the temperature dependence of the relaxation processes of STP and ion diffusion. The realization of important synaptic functions and the establishment of a dynamic model would promote more accurate modeling of the synapse for artificial neural network.

Memristor with Ag‐Cluster‐Doped TiO2 Films as Artificial Synapse for Neuroinspired Computing

“…Furthermore, the conduction of memristor cell can be tuned by using the pulse amplitude with nanosecond timescale, which is faster than many other electronic synapse. [12,25,26] By sequences of identical positive and negative voltage pulses, the conductance of our device can be well tuned repeatedly, as shown in Figure 1d.…”

“…[12,25,26] Therefore, the memristor with Ag/TiO 2 :Ag/Pt structure present a promising potential in ultrafast neuromorphic chips application. Moreover, closer spike timing will leads to a larger change of the conductance.…”

Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing

“…Various types of memristors based on ionic drift (drift-type memristor) [4][5][6][7][8] have recently been utilized for this purpose in neuromorphic architectures [9][10][11][12][13][14][15] . Although qualitative synaptic functionality has been demonstrated, the fast switching and non-volatility of drift memristors optimized for memory applications do not faithfully replicate the nature of plasticity.…”

Artificial synapse network on inorganic proton conductor for neuromorphic systems

“…In the beginning, synaptic functions were emulated by complementary metal oxide semiconductor (CMOS) neuromorphic circuits, but such CMOS circuits consumed substantially more energy than a biological synapse, and it is hard to scale up the circuits to a size comparable with the brain 5 . Recently, resistive switching memory, memristors or atomic switch has been investigated in biologically inspired neuromorphic circuits [6][7][8][9][10][11] . Important synaptic learning rules such as spike-timing-dependent plasticity (STDP) and short-term memory to long-term memory transition have been demonstrated.…”

“…Important synaptic learning rules such as spike-timing-dependent plasticity (STDP) and short-term memory to long-term memory transition have been demonstrated. In these nanoscale ionic/ electronic hybrid two-terminal artificial synapses, ionic migrant/ diffusion, formation of conducting filament and electrochemical reactions are very prevalent for mimicking the neural activities 7,10 .…”