Magnetoresistance Dynamics in Superparamagnetic 
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 Nanodots

Parks, Brad; Abdelgawad, Ahmed; Wong, Thomas; Evans, Richard F. L.; Majetich, Sara A.

doi:10.1103/physrevapplied.13.014063

Cited by 14 publications

(7 citation statements)

References 45 publications

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“…Asynchronous measurements are typically obtained for thermally unstable MTJs with < 20, where thermal fluctuations drive random switching between states, as illustrated in Fig. 2(d) [54], [55], [56], [57], [58]. These devices typically show random fluctuations near the switching fields, as illustrated in Fig.…”

Section: Tunable Stochasticity In Mtjsmentioning

confidence: 96%

Review of Magnetic Tunnel Junctions for Stochastic Computing

Zink

Wang

2022

IEEE J. Explor. Solid-State Comput. Devices Circuits

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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.INDEX TERMS Magnetic tunnel junctions (MTJs), random number generators (RNGs), spintronic devices, stochastic-bit generators (SBGs), stochastic computing (SC).

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Section: Tunable Stochasticity In Mtjsmentioning

confidence: 96%

Review of Magnetic Tunnel Junctions for Stochastic Computing

Zink

Wang

2022

IEEE J. Explor. Solid-State Comput. Devices Circuits

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“…The current I flowing in the circuit can tune the probability distribution of the resistance fluctuations, and we call such resistors tunable resistors. When designing a BSN with current tunable R, we need to know the current where fluctuations are equal between the two extreme states (I 50 ) 39 and the current required to pin the resistance to one of those states. An important parameter in this case is the bias current I 0 , which is…”

Section: (B) Current Responsementioning

confidence: 99%

“…Choice of I 50 : Another parameter that is important for the operation of tunable resistors is the I 50 which determines the midpoint of the sigmoid. I 50 is the current at which the resistance on average spends equal time in R P and R AP states 39 . As the circuit can only support positive current values, it needs to be a positive quantity and preferably matched with the saturation point (V DS = V GS ) current I Dsat of the NMOS transistor.…”

Section: Effect Of Nmentioning

confidence: 99%

Quantitative Evaluation of Hardware Binary Stochastic Neurons

Hassan

Datta

2021

Phys. Rev. Applied

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Recently there has been increasing activity to build dedicated Ising Machines to accelerate the solution of combinatorial optimization problems by expressing these problems as a ground-state search of the Ising model. A common theme of such Ising Machines is to tailor the physics of underlying hardware to the mathematics of the Ising model to improve some aspect of performance that is measured in speed to solution, energy consumption per solution or area footprint of the adopted hardware. One such approach to build an Ising spin, or a binary stochastic neuron (BSN), is a compact mixed-signal unit based on a low-barrier nanomagnet based design that uses a single magnetic tunnel junction (MTJ) and three transistors (3T-1MTJ) where the MTJ functions as a stochastic resistor (1SR). Such a compact unit can drastically reduce the area footprint of BSNs while promising massive scalability by leveraging the existing Magnetic RAM (MRAM) technology that has integrated 1T-1MTJ cells in ∼ Gbit densities. The 3T-1SR design however can be realized using different materials or devices that provide naturally fluctuating resistances. Extending previous work, we evaluate hardware BSNs from this general perspective by classifying necessary and sufficient conditions to design a fast and energy-efficient BSN that can be used in scaled Ising Machine implementations. We connect our device analysis to systems-level metrics by emphasizing hardware-independent figures-of-merit such as flips per second and dissipated energy per random bit that can be used to classify any Ising Machine.

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“…Fast fluctuations and dwell times in the nanosecond range are desired. For in-plane easy-axis MTJs (ip-MTJs), dwell times of the order of milliseconds [12], microseconds [13][14][15] down to nanoseconds [16,17] have been reported. Compared to out-of-plane MTJs (oop-MTJ), ip-MTJs often exhibit shorter dwell times, due to a different contribution of in-plane and out-of-plane anisotropy energies [18].…”

Section: Introductionmentioning

confidence: 99%

Nanosecond True Random Number Generation with Superparamagnetic Tunnel Junctions -- Identification of Joule Heating and Spin-Transfer-Torque effects

Schnitzspan¹,

Kläui²,

Jakob³

2023

Preprint

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This work investigates nanosecond superparamagnetic switching in 50 nm diameter in-plane magnetized magnetic tunnel junctions (MTJs). Due to the small in-plane uniaxial anisotropy, dwell times below 10 ns and auto-correlation times down to 5 ns are measured for circular superparamagnetic tunnel junctions (SMTJs). SMTJs exhibit probabilistic switching of the magnetic free layer, which can be used for the generation of true random numbers. The quality of random bitstreams, generated by our SMTJ, is evaluated with a statistical test suite (NIST STS, sp 800-22) and shows true randomness after three XOR operations of four random SMTJ bitstreams. A low footprint CMOS circuit is proposed for fast and energy-efficient random number generation. We demonstrate that the probability of a 1 or 0 can be tuned by spin-transfer-torque (STT), while the average bit generation rate is mainly affected by the current density via Joule heating. Although both effects are always present in MTJs, Joule heating most often is neglected. However, with a resistance area (RA) product of 15 Ωµm 2 and current densities of the order of 1 MA/cm 2 , an increasing temperature at the tunneling site results in a significant increase in the switching rate. As Joule heating and STT scale differently with current density, device design can be optimized based on our findings.

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Magnetoresistance Dynamics in Superparamagnetic Co−Fe−B Nanodots

Cited by 14 publications

References 45 publications

Review of Magnetic Tunnel Junctions for Stochastic Computing

Review of Magnetic Tunnel Junctions for Stochastic Computing

Quantitative Evaluation of Hardware Binary Stochastic Neurons

Nanosecond True Random Number Generation with Superparamagnetic Tunnel Junctions -- Identification of Joule Heating and Spin-Transfer-Torque effects

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