Optoelectronic Synapse Based on IGZO‐Alkylated Graphene Oxide Hybrid Structure

Abstract

Recently, research interest in brain-inspired neuromorphic computing based on robust and intelligent artificial neural networks has surged owing to the ability of such technology to facilitate massive parallel, low-power, highly adaptive, and event-driven computing. Here, a photosynaptic device with a novel weight updating mechanism for high-speed and low-power optoelectronic spike processing is proposed, wherein a synaptic weight is controlled by a mixed spike consisting of voltage and light spikes; the light spike, in particular, boosts up the probability of electron detrapping from graphene oxide charge-trapping layer to the photosensitive indium-gallium-zinc oxide charge-transporting layer. Compared to electrically operating synaptic device, the magnitude of conductance change in the proposed photosynaptic device increases remarkably from 2.32 to 5.95 nS without degradation of the nonlinearity (potentiation/depression values are changed from 4.24/8 to 5/8). Owing to this enhancement of synaptic operation, the recognition rates for the Modified National Institute of Standards and Technology digit patterns improve from 36% and 49% to 50% and 62% in artificial neural networks using long-term potentiation/depression characteristics with 20 and 100 weight states, respectively. The proposed photosynaptic device technology capable of optoelectronic spike processing is expected to play a crucial role in the implementation of neuromorphic computing in the future.

Recent Progress in Photonic Synapses for Neuromorphic Systems

“…Graphene, another star 2D material, also draws great attention for photonic synapses due to its superior properties of flexible, biosafe, good compatibility, and theoretically low cost . To date, graphene has been researched in photonic synapses as the floating gate and the channel material . Sun et al published an article about photonic synapses utilizing alkylated graphene as the charge trapping layer .…”

“…To date, graphene has been researched in photonic synapses as the floating gate and the channel material . Sun et al published an article about photonic synapses utilizing alkylated graphene as the charge trapping layer . Three terminal phototransistors with graphene and SWCNTs hybrid were reported by Qin et al In addition to STP induced by the heterostructure between SWCNTs and graphene and LTP induced by the trap‐rich interface between the substrate and mixed film, the device demonstrates superior sophisticated optical spike processing consisting of AND, OR, and NOR logic operations by applying different gate voltage levels.…”

Near‐Infrared‐Irradiation‐Mediated Synaptic Behavior from Tunable Charge‐Trapping Dynamics

“…To achieve artificial synaptic recognition for learning and memory in electronic devices, concomitant plasticity with different timescale is essential. [31][32][33][34] In addition, photonic synapse based on Parallel information storage coupled with storage density is a major focus for non-volatile memory devices to achieve neuromorphic computing that can work at low power. [10][11][12][13] Therefore, the emulated synaptic plasticity of STP and LTP render themselves supportive to the sophisticated cognitive function and adaptive behavior pattern.…”

“…[25][26][27][28][29][30] In contrast with the existing electrical interconnect power loss as well as the limitation in trigger selectivity and spatially confinement inherently from the computing by electric signal, emerging optical stimulation based synaptic devices can tune the synaptic plasticity enormously by photons with low-power and high-efficiency. [31][32][33][34] In addition, photonic synapse based on Parallel information storage coupled with storage density is a major focus for non-volatile memory devices to achieve neuromorphic computing that can work at low power. [31][32][33][34] In addition, photonic synapse based on Parallel information storage coupled with storage density is a major focus for non-volatile memory devices to achieve neuromorphic computing that can work at low power.…”

A Neuromorphic Device Implemented on a Salmon‐DNA Electrolyte and its Application to Artificial Neural Networks

“…[42][43][44] As shown in Figure 1a, the signal transportation mechanism of the S-DNA device resembles that of the biological synapse. [42][43][44] As shown in Figure 1a, the signal transportation mechanism of the S-DNA device resembles that of the biological synapse.…”