An ultra-compact low-power all-optical GST-based plasmonic switch for non-volatile neuromorphic synapses based on triangular cavity

Abstract Miniaturization and performance enhancement of photonic devices remain key challenges in the advancement of optical information processing technologies and photonic neural networks. In this study, an optical switch based on a triangular resonator using a metal–insulator–metal (MIM) plasmonic platform and phase-change material Ge2Sb2Te5 (GST) is introduced. By integrating a GST element with a volume of only 78,000 nm 3 into the active region of the resonator, strong electromagnetic field confinement and optimized phase transition control are achieved. This design results in an unprecedentedly small footprint (0.031 μm 2 ) and an outstanding optical contrast of 76% at the telecommunication wavelength of 1598 nm. The switching energy of this structure, estimated by numerical simulations, is about 7 pJ, placing it among the most energy-efficient photonic devices. The unique features of this structure—including the extremely small volume of active material, low energy consumption, high thermal stability, and excellent reliability—make it an ideal candidate for implementing tunable optical synapses in neuromorphic systems. Its fast nonlinear response and stable operation enable precise simulation of synaptic weighting and the implementation of learning algorithms in photonic architectures. These results demonstrate that the intelligent combination of plasmonic technology and phase-change materials in the resonator configuration can open new horizons in the design of ultra-compact, low-power photonic devices for next-generation neural networks and optical information processing.

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Publication Details

Journal
Physica Scripta
Published
2025-08-01
DOI
https://doi.org/10.1088/1402-4896/adfa4d
Primary Topic
Neural Networks and Reservoir Computing
Type
article
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article

An ultra-compact low-power all-optical GST-based plasmonic switch for non-volatile neuromorphic synapses based on triangular cavity

Parviz Keshavarzi, Mohammad Danaie, Ozra Sharifipour
Physica Scripta
Neural Networks and Reservoir Computing
article

An ultra-compact low-power all-optical GST-based plasmonic switch for non-volatile neuromorphic synapses based on triangular cavity

Parviz Keshavarzi, Mohammad Danaie, Ozra Sharifipour
article en

Abstract

Abstract Miniaturization and performance enhancement of photonic devices remain key challenges in the advancement of optical information processing technologies and photonic neural networks. In this study, an optical switch based on a triangular resonator using a metal–insulator–metal (MIM) plasmonic platform and phase-change material Ge2Sb2Te5 (GST) is introduced. By integrating a GST element with a volume of only 78,000 nm 3 into the active region of the resonator, strong electromagnetic field confinement and optimized phase transition control are achieved. This design results in an unprecedentedly small footprint (0.031 μm 2 ) and an outstanding optical contrast of 76% at the telecommunication wavelength of 1598 nm. The switching energy of this structure, estimated by numerical simulations, is about 7 pJ, placing it among the most energy-efficient photonic devices. The unique features of this structure—including the extremely small volume of active material, low energy consumption, high thermal stability, and excellent reliability—make it an ideal candidate for implementing tunable optical synapses in neuromorphic systems. Its fast nonlinear response and stable operation enable precise simulation of synaptic weighting and the implementation of learning algorithms in photonic architectures. These results demonstrate that the intelligent combination of plasmonic technology and phase-change materials in the resonator configuration can open new horizons in the design of ultra-compact, low-power photonic devices for next-generation neural networks and optical information processing.

Physica ScriptaVol. 100(8)
Semnan University (IR)
Affordable and clean energy
Openalex Percentile: Top 89%
Neural Networks and Reservoir Computing
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