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

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, 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 absence of external magnets. 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 underpins 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.