TY - JOUR
T1 - Low-Dimensional-Materials-Based Flexible Artificial Synapse
T2 - Materials, Devices, and Systems
AU - Lu, Qifeng
AU - Zhao, Yinchao
AU - Huang, Long
AU - An, Jiabao
AU - Zheng, Yufan
AU - Yap, Eng Hwa
N1 - Funding Information:
This research was funded by the National Natural Science Foundation of China (62204210), the Natural Science Foundation of Jiangsu Province (BK20220284), the Natural Science Foundation of the Higher Education Institutions of Jiangsu Province (22KJB510013, 21KJB470011), and XJTLU Research Development Funding (RDF-21-01-027).
Publisher Copyright:
© 2023 by the authors.
PY - 2023/2
Y1 - 2023/2
N2 - With the rapid development of artificial intelligence and the Internet of Things, there is an explosion of available data for processing and analysis in any domain. However, signal processing efficiency is limited by the Von Neumann structure for the conventional computing system. Therefore, the design and construction of artificial synapse, which is the basic unit for the hardware-based neural network, by mimicking the structure and working mechanisms of biological synapses, have attracted a great amount of attention to overcome this limitation. In addition, a revolution in healthcare monitoring, neuro-prosthetics, and human–machine interfaces can be further realized with a flexible device integrating sensing, memory, and processing functions by emulating the bionic sensory and perceptual functions of neural systems. Until now, flexible artificial synapses and related neuromorphic systems, which are capable of responding to external environmental stimuli and processing signals efficiently, have been extensively studied from material-selection, structure-design, and system-integration perspectives. Moreover, low-dimensional materials, which show distinct electrical properties and excellent mechanical properties, have been extensively employed in the fabrication of flexible electronics. In this review, recent progress in flexible artificial synapses and neuromorphic systems based on low-dimensional materials is discussed. The potential and the challenges of the devices and systems in the application of neuromorphic computing and sensory systems are also explored.
AB - With the rapid development of artificial intelligence and the Internet of Things, there is an explosion of available data for processing and analysis in any domain. However, signal processing efficiency is limited by the Von Neumann structure for the conventional computing system. Therefore, the design and construction of artificial synapse, which is the basic unit for the hardware-based neural network, by mimicking the structure and working mechanisms of biological synapses, have attracted a great amount of attention to overcome this limitation. In addition, a revolution in healthcare monitoring, neuro-prosthetics, and human–machine interfaces can be further realized with a flexible device integrating sensing, memory, and processing functions by emulating the bionic sensory and perceptual functions of neural systems. Until now, flexible artificial synapses and related neuromorphic systems, which are capable of responding to external environmental stimuli and processing signals efficiently, have been extensively studied from material-selection, structure-design, and system-integration perspectives. Moreover, low-dimensional materials, which show distinct electrical properties and excellent mechanical properties, have been extensively employed in the fabrication of flexible electronics. In this review, recent progress in flexible artificial synapses and neuromorphic systems based on low-dimensional materials is discussed. The potential and the challenges of the devices and systems in the application of neuromorphic computing and sensory systems are also explored.
KW - artificial perception system
KW - artificial synapse
KW - flexible electronics
KW - memristor
KW - neuromorphic computing
KW - transistor
UR - http://www.scopus.com/inward/record.url?scp=85147807377&partnerID=8YFLogxK
U2 - 10.3390/nano13030373
DO - 10.3390/nano13030373
M3 - Review article
AN - SCOPUS:85147807377
SN - 2079-4991
VL - 13
JO - Nanomaterials
JF - Nanomaterials
IS - 3
M1 - 373
ER -