Closed Loop Superparamagnetic Tunnel Junctions for Reliable True Randomness and Generative Artificial Intelligence

TL;DR

This paper demonstrates a novel closed loop three-terminal superparamagnetic tunnel junction device that reliably generates true randomness, with potential applications in secure computing and generative AI, overcoming previous scalability and reliability issues.

Contribution

The authors experimentally show a scalable, stable, and controllable superparamagnetic tunnel junction device that produces high-quality true randomness suitable for advanced computing applications.

Findings

Achieved stable, cryptographic-quality random bitstreams

Demonstrated robustness against external perturbations

Enabled generative AI applications using stochastic hardware

Abstract

Physical devices exhibiting stochastic functions with low energy consumption and high device density have the potential to enable complex probability-based computing algorithms, accelerate machine learning tasks, and enhance hardware security. Recently, superparamagnetic tunnel junctions (sMTJs) have been widely explored for such purposes, leading to the development of sMTJ-based systems; however, the reliance on nanoscale ferromagnets limits scalability and reliability, making sMTJs sensitive to external perturbations and prone to significant device variations. Here, we present an experimental demonstration of closed loop three-terminal sMTJs as reliable and potentially scalable sources of true randomness in the field-free regime. By leveraging dual-current controllability and incorporating feedback, we stabilize the switching operation of superparamagnets and reach cryptographic-quality random bitstreams. The realization of controllable and robust true random sMTJs underpin a general hardware platform for computing schemes exploiting the stochasticity in the physical world, as demonstrated by the generative artificial intelligence example in our experiment.