High-Speed Spike Rate-Coding in QD Spin-VCSELs for Neuromorphic Encoding

VCSELs are gaining significant research attention as an ideal platform for neuromorphic computing. Known for their energy efficiency and ultrafast response, VCSELs have already demonstrated promising applications in neuromorphic systems [1]. Spin-VCSELs, a specialized class of VCSELs, exploit the conservation of angular momentum to control the helicity of emitted light via spin-polarized carrier injection. These devices have been utilized in groundbreaking applications, including ultrafast data transmission [2] and reservoir computing [3]. Recently, the potential of QD spin-VCSELs has been explored for photonic spike processing, opening new avenues for ultrafast spike-based functionalities.

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Published
2025-06-23
DOI
https://doi.org/10.1109/cleo/europe-eqec65582.2025.11110797
Primary Topic
Neural Networks and Reservoir Computing
Type
article
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article

High-Speed Spike Rate-Coding in QD Spin-VCSELs for Neuromorphic Encoding

Panagiotis Georgiou, Christina Politi, Christos Tselios, Dimitris Alexandropoulos
Neural Networks and Reservoir Computing
article

High-Speed Spike Rate-Coding in QD Spin-VCSELs for Neuromorphic Encoding

Panagiotis Georgiou, Christina Politi, Christos Tselios, Dimitris Alexandropoulos
article en

Abstract

VCSELs are gaining significant research attention as an ideal platform for neuromorphic computing. Known for their energy efficiency and ultrafast response, VCSELs have already demonstrated promising applications in neuromorphic systems [1]. Spin-VCSELs, a specialized class of VCSELs, exploit the conservation of angular momentum to control the helicity of emitted light via spin-polarized carrier injection. These devices have been utilized in groundbreaking applications, including ultrafast data transmission [2] and reservoir computing [3]. Recently, the potential of QD spin-VCSELs has been explored for photonic spike processing, opening new avenues for ultrafast spike-based functionalities.

University of Peloponnese (GR), University of Patras (GR)
Affordable and clean energy
Openalex Percentile: Top 89%
Neural Networks and Reservoir Computing
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