Securing Global Supply Chain with nanophotonic Hardware Security solutions
Primary Investigator:
Peter Bermel
Haimabati Dey, Jie Zhu, Peter Bermel, Dept. of ECE & Birck Nanotechnology Center
Abstract
With the increasing complexity of Global Supply Chain management, there is currently an urgent need to develop secure wireless Tagging of imported and exported components for industrial asset management during transportation for the assembly process. Existing RFID tags often may not be adequate for application specific devices and components that are smaller in size and demonstrate sensitivity to radiofrequency signaling. Additionally, MOS-based hardware security solutions are currently facing vulnerabilities from machine learning attacks that can intercept cryptographic challenge-response pairs of such electronic circuits and predict future challenge-response pairs applying advanced nano-electronic device-modelling with high computation speed. To overcome this, we present a novel nonlinear nano-photonic Ring Resonator Physically Unclonable Functions that can be embedded with such devices under transit and perform wireless tagging and sensing operation to maintain safety and integrity of these devices against duplication, counterfeiting or damage by external attackers. Our proposed Ring Resonator Physically Unclonable Functions has inherent metallic scattering mechanisms that are difficult to model inversely from input/output response pairs of those functions through reverse-engineering. These nanophotonic PUFs generate device-specific reproducible authentication keys from the internal manufacturing variability of the bus and rings which further affect photonic signal processing of the overall structure. Designing integrated photonic circuits containing such ring resonators can generate frequency-dependent unique, statistically diverse yet reproducible pseudo-random photonic output responses while excited upon different pulse widths that shows significant variability and hence robustness against guessing attacks. Complexity in modelling the behavior of such cost-effective nanophotonic structures and yet ease in manufacturing them provides our design secure against machine learning attacks providing unpredictability of cryptographic challenge-response pairs. By improving on mathematical complexity of the cryptographic authentication with cost-efficient nanophotonic designs, we can achieve significant advantage over existing nano-electronics cryptographic hardware as well as software security protocols in securing next-generation Industrial Asset Tagging Solutions.