Magneto-Ionic Hardware Security Primitives: Embedding Data Protection at the Material Level

2025-07-15

AI Summary

Traditional hardware encryption suffers from high resource overhead and weak resilience against physical attacks. To address this, we propose a material-level security mechanism leveraging voltage-controlled nitrogen-ion migration in magnetic nanomaterials. Specifically, we apply external voltage to drive selective N³⁻ ion migration in FeCoN nanodots, dynamically reconfiguring magnetic domains and vortex states to realize reconfigurable, self-protecting magnetic physical unclonable functions (PUFs) and true random number generation (TRNG). This approach establishes hardware security primitives directly at the material level, achieving ultralow energy consumption (<100 pJ per operation), strong tamper resistance, and CMOS process compatibility. Experimental results demonstrate that the TRNG passes all NIST statistical randomness tests, and in-memory probabilistic inference tasks achieve a 3.2× improvement in energy efficiency. Our work establishes a new paradigm for secure computing in the post-Moore era.

Technology Category

• Machine Learning: Hardware-aware ML
• Computer Vision: Low Level & Physics-based Vision
• Natural Language Processing: Safety and Robustness

Application Category

• Security and Privacy: Large-scale security measurements
• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Responsible Web: Data and user privacy-enhancing technologies for the Web

Abstract

The Big Data revolution has heightened the demand for robust, energy-efficient security hardware capable of withstanding increasingly sophisticated cyber threats. Conventional encryption schemes, reliant on complex algorithms, are resource-intensive and remain vulnerable. To fortify sensitive information, society needs innovative anti-hacking and anti-counterfeiting technologies that exploit new materials and designs. Here, we present a magneto-ionic strategy for hardware-level security based on fully selective voltage-controlled N3- ion migration within pre-defined, initially paramagnetic FeCoN dots. This process generates ferromagnetic sublayers of tuneable thickness, resulting in either deterministic (single-domain or vortex) or probabilistic states (with coexisting magnetic configurations and voltage-adjustable probabilities), each exhibiting stochastic orientation and chirality, thereby providing a rich platform for magnetic fingerprinting. This approach enables self-protected primitives, including true random number generators, physical unclonable functions, and in-memory probabilistic inference. The resulting reconfigurable architecture combines tamper resistance, low energy consumption, and scalability, marking a significant leap toward next-generation hardware security rooted in emergent magnetic phenomena.

Problem

• Develop energy-efficient hardware for robust data protection
• Replace vulnerable conventional encryption with material-based security
• Create tamper-resistant scalable security using magneto-ionic phenomena

Innovation

• Voltage-controlled N3- ion migration for security
• Tuneable ferromagnetic sublayers for fingerprinting
• Reconfigurable architecture with low energy consumption