On-chip optical memristors based on ferroelectric-doped graphene

Chip-integrated optical memristors, modulating light in a nonvolatile and semicontinuous manner, are attractive to revolutionize on-chip optical signal processing via the constructions of nonvolatile reconfigurable photonic circuits, in-memory computing, brain-inspired architectures, etc. Mechanisms, including phase-change, filamentation, and ferroelectricity, have been attempted to implement on-chip optical memristors, though their intricate tradeoffs between fabrication compatibility, modulation depth, power consumption, retention time, and cyclability make it desired to pursue new architectures. Here, we demonstrate graphene-based on-chip optical amplitude and phase memristors by electrostatically doping the graphene integrated on a silicon nitride waveguide with a ferroelectric film. Benefiting from graphene’s significant dependence of complex refractive index on its carrier density and the ferroelectric remnant doping, semicontinuous nonvolatile modulation with a maximum depth of ∼32.5dB is realized with a low programming energy of ∼1.86pJ/µm 2 , exhibiting good cyclability (fluctuation ratio <0.9%) and long retention time (over 10 years). By integrating the graphene-based optical memristor with cascaded microring resonators, in-memory computings with multiple wavelength channels are demonstrated by analogue matrix-vector multiplication and digital logic gate operations. Combining these merits with CMOS-compatible on-chip graphene integration, the demonstrated graphene-based optical memristor has proven to be a competitive candidate for high-bandwidth neuromorphic computing, convolutional processing, and artificial intelligence on photonic integrated circuits.

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Publication Details

Journal
Optica
Published
2024-12-20
DOI
https://doi.org/10.1364/optica.543416
Citations
3
Primary Topic
Advanced Memory and Neural Computing
Type
article
Field-Weighted Citation Impact
0.63
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article

On-chip optical memristors based on ferroelectric-doped graphene

Ruijuan Tian, Zheng‐Dong Luo, Xiaomu Wang, Xuetao Gan et al.
3 citations
Optica
Advanced Memory and Neural Computing
0.63
article

On-chip optical memristors based on ferroelectric-doped graphene

Ruijuan Tian, Zheng‐Dong Luo, Xiaomu Wang, Xuetao Gan, Genquan Han, Yue Hao, Jianguo Wang, Yan Liu, Bing Chen, Danyang Yao, Y. Zhang
article en
3 citations

Abstract

Chip-integrated optical memristors, modulating light in a nonvolatile and semicontinuous manner, are attractive to revolutionize on-chip optical signal processing via the constructions of nonvolatile reconfigurable photonic circuits, in-memory computing, brain-inspired architectures, etc. Mechanisms, including phase-change, filamentation, and ferroelectricity, have been attempted to implement on-chip optical memristors, though their intricate tradeoffs between fabrication compatibility, modulation depth, power consumption, retention time, and cyclability make it desired to pursue new architectures. Here, we demonstrate graphene-based on-chip optical amplitude and phase memristors by electrostatically doping the graphene integrated on a silicon nitride waveguide with a ferroelectric film. Benefiting from graphene’s significant dependence of complex refractive index on its carrier density and the ferroelectric remnant doping, semicontinuous nonvolatile modulation with a maximum depth of ∼32.5dB is realized with a low programming energy of ∼1.86pJ/µm 2 , exhibiting good cyclability (fluctuation ratio <0.9%) and long retention time (over 10 years). By integrating the graphene-based optical memristor with cascaded microring resonators, in-memory computings with multiple wavelength channels are demonstrated by analogue matrix-vector multiplication and digital logic gate operations. Combining these merits with CMOS-compatible on-chip graphene integration, the demonstrated graphene-based optical memristor has proven to be a competitive candidate for high-bandwidth neuromorphic computing, convolutional processing, and artificial intelligence on photonic integrated circuits.

OpticaVol. 12(1)
Xuzhou University of Technology (CN), Xidian University (CN), Northwestern Polytechnical University (CN), Nanjing University (CN)
Openalex Percentile: Top 30%
Advanced Memory and Neural Computing
0.63
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