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Journal ArticleDOI

Machine Learning for Statistical Modeling: The Case of Perpendicular Spin-Transfer-Torque Random Access Memory

TL;DR: In this article, a methodology to perform process variation-aware device and circuit design using fully physics-based simulations within limited computational resources, without developing a compact model, was proposed.
Abstract: We propose a methodology to perform process variation-aware device and circuit design using fully physics-based simulations within limited computational resources, without developing a compact model. Machine learning (ML), specifically a support vector regression (SVR) model, has been used. The SVR model has been trained using a dataset of devices simulated a priori, and the accuracy of prediction by the trained SVR model has been demonstrated. To produce a switching time distribution from the trained ML model, we only had to generate the dataset to train and validate the model, which needed ∼500 hours of computation. On the other hand, if 106 samples were to be simulated using the same computation resources to generate a switching time distribution from micromagnetic simulations, it would have taken ∼250 days. Spin-transfer-torque random access memory (STTRAM) has been used to demonstrate the method. However, different physical systems may be considered, different ML models can be used for different physical systems and/or different device parameter sets, and similar ends could be achieved by training the ML model using measured device data.
Citations
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Journal ArticleDOI
TL;DR: In this paper, a post-CMOS magneto-electric FET (MEFET) is proposed for high-speed and low-power design in both logic and memory applications.
Abstract: Magneto-Electric FET (MEFET) is a recently developed post-CMOS FET, which offers intriguing characteristics for high-speed and low-power design in both logic and memory applications. In this articl...

5 citations

Journal ArticleDOI
TL;DR: In this article , a finite-temperature micromagnetic study of magnetization switching and write-error rates in a perpendicular magnetic tunnel junction with and without synthetic antiferromagnetic layer (SAF) is presented.
Abstract: A finite-temperature micromagnetic study of magnetization switching and write-error rates in a perpendicular magnetic tunnel junction with and without synthetic antiferromagnetic layer (SAF) is presented. In the absence of SAF, magnetization switching is induced by domain-wall nucleation and propagation. Although the various modes of domain-wall propagation are observed to delay switching, it does not show an appreciable impact on the overall write-error-rate slopes. In the presence of the nonuniform stray field from the SAF assembly, the domain-wall-based switching modes turn on more complex magnetization dynamics that impedes the switching process. In cases where the SAF layers fail to balance each other contributing to a stronger stray field, incoherent switching modes give rise to metastable states with significantly longer lifetimes, and a dramatic change in the write-error slopes is observed. Simulation results are compared to recent experimental findings from time-domain measurements of spin-transfer-torque switching and measurements of anomalous write-error rates. These results directly prove the long-predicted relation of the SAF stray field to write-error anomaly in perpendicular spin-transfer-torque magnetic random-access memory and could be useful to solve the anomalous write-error problems.
References
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Journal ArticleDOI
TL;DR: Inter interfacial perpendicular anisotropy between the ferromagnetic electrodes and the tunnel barrier of the MTJ is used by employing the material combination of CoFeB-MgO, a system widely adopted to produce a giant tunnel magnetoresistance ratio in MTJs with in-plane an isotropy.
Abstract: Magnetic tunnel junctions (MTJs) with ferromagnetic electrodes possessing a perpendicular magnetic easy axis are of great interest as they have a potential for realizing next-generation high-density non-volatile memory and logic chips with high thermal stability and low critical current for current-induced magnetization switching. To attain perpendicular anisotropy, a number of material systems have been explored as electrodes, which include rare-earth/transition-metal alloys, L1(0)-ordered (Co, Fe)-Pt alloys and Co/(Pd, Pt) multilayers. However, none of them so far satisfy high thermal stability at reduced dimension, low-current current-induced magnetization switching and high tunnel magnetoresistance ratio all at the same time. Here, we use interfacial perpendicular anisotropy between the ferromagnetic electrodes and the tunnel barrier of the MTJ by employing the material combination of CoFeB-MgO, a system widely adopted to produce a giant tunnel magnetoresistance ratio in MTJs with in-plane anisotropy. This approach requires no material other than those used in conventional in-plane-anisotropy MTJs. The perpendicular MTJs consisting of Ta/CoFeB/MgO/CoFeB/Ta show a high tunnel magnetoresistance ratio, over 120%, high thermal stability at dimension as low as 40 nm diameter and a low switching current of 49 microA.

3,169 citations

Journal ArticleDOI
TL;DR: In this paper, the authors report on the design, verification and performance of mumax3, an open-source GPU-accelerated micromagnetic simulation program that solves the time and space dependent magnetization evolution in nano-to micro-scale magnets using a finite-difference discretization.
Abstract: We report on the design, verification and performance of mumax3, an open-source GPU-accelerated micromagnetic simulation program. This software solves the time- and space dependent magnetization evolution in nano- to micro scale magnets using a finite-difference discretization. Its high performance and low memory requirements allow for large-scale simulations to be performed in limited time and on inexpensive hardware. We verified each part of the software by comparing results to analytical values where available and to micromagnetic standard problems. mumax3 also offers specific extensions like MFM image generation, moving simulation window, edge charge removal and material grains.

2,209 citations

Journal ArticleDOI
TL;DR: The design, verification and performance of MUMAX3, an open-source GPU-accelerated micromagnetic simulation program that solves the time- and space dependent magnetization evolution in nano- to micro scale magnets using a finite-difference discretization is reported on.
Abstract: We report on the design, verification and performance of MUMAX3, an open-source GPU-accelerated micromagnetic simulation program. This software solves the time- and space dependent magnetization evolution in nano- to micro scale magnets using a finite-difference discretization. Its high performance and low memory requirements allow for large-scale simulations to be performed in limited time and on inexpensive hardware. We verified each part of the software by comparing results to analytical values where available and to micromagnetic standard problems. MUMAX3 also offers specific extensions like MFM image generation, moving simulation window, edge charge removal and material grains.

2,116 citations

Journal ArticleDOI
TL;DR: Scikit-learn as mentioned in this paper is a Python module integrating a wide range of state-of-the-art machine learning algorithms for medium-scale supervised and unsupervised problems.
Abstract: Scikit-learn is a Python module integrating a wide range of state-of-the-art machine learning algorithms for medium-scale supervised and unsupervised problems. This package focuses on bringing mach...

1,787 citations

Journal ArticleDOI
Jonathan Z. Sun1
TL;DR: In this paper, the authors examined the consequence of spin-current-induced angular momentum deposition in a monodomain Stoner-Wohlfarth magnetic body using the Landau-Lifshitz-Gilbert equation with a phenomenological damping coefficient.
Abstract: I examined the consequence of a spin-current-induced angular momentum deposition in a monodomain Stoner-Wohlfarth magnetic body. The magnetic dynamics of the particle are modeled using the Landau-Lifshitz-Gilbert equation with a phenomenological damping coefficient $\ensuremath{\alpha}.$ Two magnetic potential landscapes are studied in detail: One uniaxial, the other uniaxial in combination with an easy-plane potential term that could be used to model a thin-film geometry with demagnetization. Quantitative predictions are obtained for comparison with experiments.

1,075 citations