Optoelectronic resistive random access memory for neuromorphic vision sensors

Neuromorphic visual systems have considerable potential to emulate basic functions of the human visual system even beyond the visible light region. However, the complex circuitry of artificial visual systems based on conventional image sensors, memory and processing units presents serious challenges...

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Vydáno v:Nature nanotechnology Ročník 14; číslo 8; s. 776 - 782
Hlavní autoři: Zhou, Feichi, Zhou, Zheng, Chen, Jiewei, Choy, Tsz Hin, Wang, Jingli, Zhang, Ning, Lin, Ziyuan, Yu, Shimeng, Kang, Jinfeng, Wong, H-S Philip, Chai, Yang
Médium: Journal Article
Jazyk:angličtina
Vydáno: England Nature Publishing Group 01.08.2019
Témata:
Artificial Organs
BASIC (programming language)
Bionics - instrumentation
Circuits
Edge computing
Equipment Design
Flight simulation
Humans
Information processing
Internet of Things
Light
Memory devices
Object recognition
Optoelectronic devices
Power consumption
Random access memory
Sensors
Vision, Ocular
Visual system
ISSN:1748-3387, 1748-3395, 1748-3395
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Shrnutí:Neuromorphic visual systems have considerable potential to emulate basic functions of the human visual system even beyond the visible light region. However, the complex circuitry of artificial visual systems based on conventional image sensors, memory and processing units presents serious challenges in terms of device integration and power consumption. Here we show simple two-terminal optoelectronic resistive random access memory (ORRAM) synaptic devices for an efficient neuromorphic visual system that exhibit non-volatile optical resistive switching and light-tunable synaptic behaviours. The ORRAM arrays enable image sensing and memory functions as well as neuromorphic visual pre-processing with an improved processing efficiency and image recognition rate in the subsequent processing tasks. The proof-of-concept device provides the potential to simplify the circuitry of a neuromorphic visual system and contribute to the development of applications in edge computing and the internet of things.
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ISSN:1748-3387
1748-3395
1748-3395
DOI:10.1038/s41565-019-0501-3