Artificial synapses enabled neuromorphic computing: From blueprints to reality. (1st December 2022) - Li, Junyan; Shen, Zongjie; Cao, Yixin; Tu, Xin; Zhao, Chun; Liu, Yina; Wen, Zhen
Abstract: Emerging brain-inspired neuromorphic computing systems have become a potential candidate for overcoming the von Neuman bottleneck that limits the performance of most modern computers. Artificial synapses, used to mimic neural transmission and physical information sensing, could build highly robust and efficient computing systems similar to our brains. The employment of nanomaterials in the devices, and the device structures, are receiving a surge of interest, given the various benefits in better carrier dynamics, higher conductance, photonic interaction and photocarrier trapping, and the architectural feasibility with two and three-terminal devices. Moreover, the combination of artificial synapses and various nanomaterial-based active channels also enables visual recognition, multi-modality sensing-processing systems, hardware neural networks, etc., demonstrating appealing possibilities for practical applications. Here, we summarize the recent advances in synaptic devices based on low-dimensional nanomaterials, the novel devices with hybrid materials or structures, as well as implementation schemes of hardware neural networks. By the end of this review, we discuss the engineering issues including control methods, design complexity and fabrication process to be addressed, and envision the future developments of artificial synapse-based neuromorphic systems. Graphical Abstract: We reviewed recent advancements of artificial synaptic devices made from low-dimensional materials and multi-stimuli devices combined with TENGs, combining with innovative neuromorphic applications and feasible hardware implementations based on these synaptic devices. This work provides a comprehensive review of the evolving low-dimensional material-based artificial synaptic devices and advancing neuromorphic hardware based on synaptic devices and outlooks to their industrialization, revealing a glimpse of the promising future of artificial synapse-based neuromorphic computing. ga1 Highlights: We reviewed the recent advancements in artificial synaptic devices with low-dimensional materials (2D, 1D, and hybrid 0D). Multi-modality synaptic devices combined with triboelectric nanogenerators (TENGs) are also introduced. We innovatively combine the review of device advancements with insights on evolving neuromorphic applications and hardware. This work provides a comprehensive review of the renewing low-dimensional material-based artificial synaptic devices. This work summarizes advancing neuromorphic hardware based on synaptic devices and outlooks to their industrialization.