Reduced sensitivity to process, voltage and temperature variations in activated perpendicular magnetic tunnel junctions based stochastic devices

TL;DR

This paper analyzes medium-barrier magnetic tunnel junctions operated with nanosecond pulses, demonstrating they are less sensitive to process, voltage, and temperature variations, and are energy-efficient for true random number generation.

Contribution

It provides a systematic numerical analysis of MBM-based pMTJs, showing their robustness and efficiency as TRNGs under PVT variations using macrospin dynamics simulations.

Findings

SMART devices with <1 ns pulses are less sensitive to PVT variations.

Short pulses consume less energy (~fJ) compared to longer pulses.

Results support development of fast, robust, and energy-efficient TRNG hardware.

Abstract

True random number generators (TRNGs) are fundamental building blocks for many applications, such as cryptography, Monte Carlo simulations, neuromorphic computing, and probabilistic computing. While perpendicular magnetic tunnel junctions (pMTJs) based on low-barrier magnets (LBMs) are natural sources of TRNGs, they tend to suffer from device-to-device variability, low speed, and temperature sensitivity. Instead, medium-barrier magnets (MBMs) operated with nanosecond pulses - denoted, stochastic magnetic actuated random transducer (SMART) devices - are potentially superior candidates for such applications. We present a systematic analysis of spin-torque-driven switching of MBM-based pMTJs (Eb ~ 20 - 40 kBT) as a function of pulse duration (1 ps to 1 ms), by numerically solving their macrospin dynamics using a 1-D Fokker-Planck equation. We investigate the impact of voltage, temperature, and process variations (MTJ dimensions and material parameters) on the switching probability of the device. Our findings indicate SMART devices activated by short-duration pulses (< 1 ns) are much less sensitive to process-voltage-temperature (PVT) variations while consuming lower energy (~ fJ) than the same devices operated with longer pulses. Our results show a path toward building fast, energy-efficient, and robust TRNG hardware units for solving optimization problems.