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Optical Authentication Leveraging Chaotic-Cavity VCSEL Far-Field Patterns with Deep Learning

  • Hang Lu
  • , Zhican Zhou
  • , Juan M. Marin
  • , Chun Hong Kang
  • , Omar Alkhazragi
  • , Heming Lin
  • , Islam Ashry
  • , Tien Khee Ng
  • , Yating Wan
  • , Boon S. Ooi*
  • *Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Scopus citations

Abstract

We present an optical authentication system that leverages far-field patterns of chaotic-cavity VCSELs and real-time CNN-based recognition, achieving 100 % identification accuracy with high robustness to misalignment. This scalable and cost-effective approach promises secure and practical anticounterfeit and authentication solutions for next-generation consumer electronics.

Original languageEnglish
Title of host publication2025 IEEE Photonics Conference, IPC 2025 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9798331525590
DOIs
StatePublished - 2025
Externally publishedYes
Event2025 IEEE Photonics Conference, IPC 2025 - Singapore, Singapore
Duration: 9 Nov 2025 → 13 Nov 2025

Publication series

Name2025 IEEE Photonics Conference, IPC 2025 - Proceedings

Conference

Conference2025 IEEE Photonics Conference, IPC 2025
Country/TerritorySingapore
CitySingapore
Period9/11/25 → 13/11/25

Bibliographical note

Publisher Copyright:
© 2025 IEEE.

Keywords

  • VCSEL
  • chaotic cavity
  • optical authentication

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Hardware and Architecture
  • Signal Processing
  • Electrical and Electronic Engineering
  • Electronic, Optical and Magnetic Materials
  • Atomic and Molecular Physics, and Optics

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