AI-guided framework for the design of materials and devices for magnetic-tunnel-junction-based true random number generators.

Emerging devices, such as magnetic tunnel junctions, are key for energy-efficient, performant future computing systems. However, designing devices with the desirable specification and performance for these applications is often found to be time-consuming and non-trivial. Here, we investigate the design and optimization of spin-orbit torque and spin transfer torque magnetic tunnel junction models as the probabilistic devices for true random number generation.

Magnetic tunnel junction random number generators applied to dynamically tuned probability trees driven by spin orbit torque.

Perpendicular magnetic tunnel junction (pMTJ)-based true-random number generators (RNGs) can consume orders of magnitude less energy per bit than CMOS pseudo-RNGs. Here, we numerically investigate with a macrospin Landau-Lifshitz-Gilbert equation solver the use of pMTJs driven by spin-orbit torque to directly sample numbers from arbitrary probability distributions with the help of a tunable probability tree. The tree operates by dynamically biasing sequences of pMTJ relaxation events, called 'coinflips', via an additional applied spin-transfer-torque current.