Review of Magnetic Tunnel Junctions for Stochastic Computing

Brandon R Zink, Yang Lv, Jianping Wang

Research output: Contribution to journalArticlepeer-review

7 Scopus citations


Modern computing schemes require large circuit areas and large energy consumption for neuromorphic computing applications, such as recognition, classification, and prediction. This is because these tasks require parallel processing on large datasets. Stochastic computing (SC) is a promising alternative to conventional binary computing schemes due to its low area cost, low processing power, and robustness to noise. However, the large area and energy costs for random number generation with CMOS-based circuits make SC impractical for most hardware implementations. For this reason, beyond-CMOS approaches to random number generation have been investigated in recent years. Spintronics is one of the most promising approaches due to the intrinsic stochasticity of the magnetic tunnel junction (MTJ). In this review article, we provide an overview of the literature published in recent years investigating the tunable, intrinsic stochasticity of MTJs and proposing practical methods for random number generation using spintronic hardware.

Original languageEnglish (US)
Pages (from-to)173-184
Number of pages12
JournalIEEE Journal on Exploratory Solid-State Computational Devices and Circuits
Issue number2
StatePublished - Dec 1 2022

Bibliographical note

Publisher Copyright:
© 2022 IEEE.


  • Magnetic tunnel junctions (MTJs)
  • random number generators (RNGs)
  • spintronic devices
  • stochastic computing (SC)
  • stochastic-bit generators (SBGs)


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