Closed Loop Superparamagnetic Tunnel Junctions for Reliable True Randomness and Generative Artificial Intelligence
<|reference_start|>Closed Loop Superparamagnetic Tunnel Junctions for Reliable True Randomness and Generative Artificial Intelligence: 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.<|reference_end|>
@article{koh2024closed,
    title={Closed Loop Superparamagnetic Tunnel Junctions for Reliable True
  Randomness and Generative Artificial Intelligence},
    author={Dooyong Koh, Qiuyuan Wang, Brooke C. McGoldrick, Chung-Tao Chou,
  Luqiao Liu, Marc A. Baldo},
    journal={arXiv preprint arXiv:2407.08665},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.08665},
    primaryClass={cond-mat.mtrl-sci cs.ET}
}