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Published on: January 3, 2016
Noise-Resilient Plasmonic Physical Unclonable Functions via Deep Contrastive Learning
Junyuan Lu1, Ruhaan Batta1, Jingang Li1
1School of Mechanical Engineering and School of Materials Engineering, Purdue University, West Lafayette, Indiana47907, United States.
Nano Letters
|July 22, 2026
Summary
This study introduces a novel plasmonic Physical Unclonable Function (PUF) for hardware security. The system achieves high accuracy and robustness against imaging variations, enabling secure authentication.
Area of Science:
- Nanomaterials science
- Hardware security
- Optics
Background:
- Physical unclonable functions (PUFs) are crucial for hardware security due to their unique challenge-response behavior.
- Optical PUFs offer large encoding spaces but face reliability issues from imaging inconsistencies like vibrations and illumination changes.
Purpose of the Study:
- To develop a reliable plasmonic PUF system using gold nanoislands.
- To enhance the robustness and accuracy of optical PUF identification against environmental variations.
Main Methods:
- Fabrication of a plasmonic PUF using spatially disordered gold nanoislands via polymer-mediated dewetting.
- Training a ConvNeXt-based encoder with combined supervised contrastive, circle, and uniformity losses.
- Evaluating system performance under color perturbations and region-of-interest shifts.
Main Results:
- Achieved an identification accuracy of 99.9% for the plasmonic PUF system.
- Demonstrated robust discrimination under up to 20% color perturbations and region-of-interest shifts.
- Outperformed traditional direct binarization methods in reliability.
Conclusions:
- The developed plasmonic PUF system offers a reliable and accurate hardware security primitive.
- On-chip integration capabilities highlight a scalable pathway for practical hardware security applications.
- The system shows promise for both one-time and reusable authentication.
