Superparamagnetic Tunnel Junctions for Reliable True Randomness and Efficient Probabilistic Machine Learning

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 limited-scale sMTJ-based systems. Existing sMTJs face significant scalability and reliability issues, however, because their intrinsically low energy barrier and correspondingly small device area result in high sensitivity to external perturbations, as well as large variations from device to device. Here, we present an experimental demonstration of 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. Furthermore, we experimentally demonstrate a novel method of utilizing sMTJs as stochastic analog-to-digital converters (sADCs) in a crossbar array architecture for neural network acceleration, showing performance comparable to software implementations. This work highlights the potential of sMTJs to revolutionize energy-efficient computing and provides a foundation for future advancements in probabilistic computing and hardware security.