Analytical Macrospin Modeling of the Stochastic Switching Time of Spin-Transfer Torque Devices

Owing to their nonvolatility, outstanding endurance, high write and read speeds, and CMOS process compatibility, spin-transfer torque magnetoresistive memories (MRAMs) are prime candidates for innovative memory applications. However, the switching delay of their core components-the magnetic tunnel j...
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Autor*in:	Vincent, Adrien F [verfasserIn] Locatelli, Nicolas Klein, Jacques-Olivier Zhao, Weisheng S Galdin-Retailleau, Sylvie Querlioz, Damien

Format:	Artikel
Sprache:	Englisch

Erschienen:	2015

Schlagwörter:	MRAM simulation Stochastic processes spin-transfer torque magnetoresistive memories magnetic tunnel junctions in-plane magnetization MRAM devices probability physical macrospin simulations magnetic memories Switches stochastic switching delay CMOS process compatibility Mathematical model Delays current-driven MTJ intrinsic stochastic behavior mean switching magnetisation Current density partial models modeling Magnetic devices error rate Analytical models magnetic tunnelling Magnetic tunneling probability distribution Computer memory Random access memory Design Models Magnetoresistance Analysis Magnetic tunnel junctions Distribution (Probability theory) Stochastic analysis Complementary metal oxide semiconductors Design and construction Usage

Übergeordnetes Werk:	Enthalten in: IEEE transactions on electron devices - New York, NY : IEEE, 1963, 62(2015), 1, Seite 164-170
Übergeordnetes Werk:	volume:62 ; year:2015 ; number:1 ; pages:164-170

Links:	Volltext Link aufrufen Link aufrufen

DOI / URN:	10.1109/TED.2014.2372475

Katalog-ID:	OLC1967766029

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520			\|a Owing to their nonvolatility, outstanding endurance, high write and read speeds, and CMOS process compatibility, spin-transfer torque magnetoresistive memories (MRAMs) are prime candidates for innovative memory applications. However, the switching delay of their core components-the magnetic tunnel junctions (MTJs)-is a stochastic quantity. To account for this in electronic design, only partial models (working in extreme regimes) are available. In this paper, we propose an analytical model for the stochastic switching delay of a current-driven MTJ, with in-plane magnetization, that agrees with physical simulations, from low- to high-current regimes through intermediate regime. We performed physical macrospin simulations of MTJs for a wide range of current. We developed an analytical model for the mean switching delay that fits those simulations results, and smoothly connects well-accepted models for the extreme low and extreme high currents limits. In addition, a probability distribution in agreement with our simulations results is proposed, leading to a full model of the stochastic switching delay. An example for the application of the model is proposed. Our analytical model can help to evaluate the error rate in MRAM designs, and allow designing innovative electronic circuits that exploit the intrinsic stochastic behavior of MTJs as a beneficial feature.
650		4	\|a MRAM
650		4	\|a simulation
650		4	\|a Stochastic processes
650		4	\|a spin-transfer torque magnetoresistive memories
650		4	\|a magnetic tunnel junctions
650		4	\|a in-plane magnetization
650		4	\|a MRAM devices
650		4	\|a probability
650		4	\|a physical macrospin simulations
650		4	\|a magnetic memories
650		4	\|a Switches
650		4	\|a stochastic switching delay
650		4	\|a CMOS process compatibility
650		4	\|a Mathematical model
650		4	\|a Delays
650		4	\|a current-driven MTJ
650		4	\|a intrinsic stochastic behavior
650		4	\|a mean switching
650		4	\|a magnetisation
650		4	\|a Current density
650		4	\|a partial models
650		4	\|a modeling
650		4	\|a Magnetic devices
650		4	\|a error rate
650		4	\|a Analytical models
650		4	\|a magnetic tunnelling
650		4	\|a Magnetic tunneling
650		4	\|a probability distribution
650		4	\|a Computer memory
650		4	\|a Random access memory
650		4	\|a Design
650		4	\|a Models
650		4	\|a Magnetoresistance
650		4	\|a Analysis
650		4	\|a Magnetic tunnel junctions
650		4	\|a Distribution (Probability theory)
650		4	\|a Stochastic analysis
650		4	\|a Complementary metal oxide semiconductors
650		4	\|a Design and construction
650		4	\|a Usage
700	1		\|a Locatelli, Nicolas \|4 oth
700	1		\|a Klein, Jacques-Olivier \|4 oth
700	1		\|a Zhao, Weisheng S \|4 oth
700	1		\|a Galdin-Retailleau, Sylvie \|4 oth
700	1		\|a Querlioz, Damien \|4 oth
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allfields	10.1109/TED.2014.2372475 doi PQ20160617 (DE-627)OLC1967766029 (DE-599)GBVOLC1967766029 (PRQ)c2574-c32dbd5c0078f165ffbc2eb01262c23a945176fb4219fe51c786a432ea81282c0 (KEY)0079428720150000062000100164analyticalmacrospinmodelingofthestochasticswitchin DE-627 ger DE-627 rakwb eng 620 DNB Vincent, Adrien F verfasserin aut Analytical Macrospin Modeling of the Stochastic Switching Time of Spin-Transfer Torque Devices 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier Owing to their nonvolatility, outstanding endurance, high write and read speeds, and CMOS process compatibility, spin-transfer torque magnetoresistive memories (MRAMs) are prime candidates for innovative memory applications. However, the switching delay of their core components-the magnetic tunnel junctions (MTJs)-is a stochastic quantity. To account for this in electronic design, only partial models (working in extreme regimes) are available. In this paper, we propose an analytical model for the stochastic switching delay of a current-driven MTJ, with in-plane magnetization, that agrees with physical simulations, from low- to high-current regimes through intermediate regime. We performed physical macrospin simulations of MTJs for a wide range of current. We developed an analytical model for the mean switching delay that fits those simulations results, and smoothly connects well-accepted models for the extreme low and extreme high currents limits. In addition, a probability distribution in agreement with our simulations results is proposed, leading to a full model of the stochastic switching delay. An example for the application of the model is proposed. Our analytical model can help to evaluate the error rate in MRAM designs, and allow designing innovative electronic circuits that exploit the intrinsic stochastic behavior of MTJs as a beneficial feature. MRAM simulation Stochastic processes spin-transfer torque magnetoresistive memories magnetic tunnel junctions in-plane magnetization MRAM devices probability physical macrospin simulations magnetic memories Switches stochastic switching delay CMOS process compatibility Mathematical model Delays current-driven MTJ intrinsic stochastic behavior mean switching magnetisation Current density partial models modeling Magnetic devices error rate Analytical models magnetic tunnelling Magnetic tunneling probability distribution Computer memory Random access memory Design Models Magnetoresistance Analysis Magnetic tunnel junctions Distribution (Probability theory) Stochastic analysis Complementary metal oxide semiconductors Design and construction Usage Locatelli, Nicolas oth Klein, Jacques-Olivier oth Zhao, Weisheng S oth Galdin-Retailleau, Sylvie oth Querlioz, Damien oth Enthalten in IEEE transactions on electron devices New York, NY : IEEE, 1963 62(2015), 1, Seite 164-170 (DE-627)129602922 (DE-600)241634-7 (DE-576)015096734 0018-9383 nnns volume:62 year:2015 number:1 pages:164-170 http://dx.doi.org/10.1109/TED.2014.2372475 Volltext http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6981938 http://search.proquest.com/docview/1640794838 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_170 GBV_ILN_2004 GBV_ILN_4313 GBV_ILN_4314 AR 62 2015 1 164-170
spelling	10.1109/TED.2014.2372475 doi PQ20160617 (DE-627)OLC1967766029 (DE-599)GBVOLC1967766029 (PRQ)c2574-c32dbd5c0078f165ffbc2eb01262c23a945176fb4219fe51c786a432ea81282c0 (KEY)0079428720150000062000100164analyticalmacrospinmodelingofthestochasticswitchin DE-627 ger DE-627 rakwb eng 620 DNB Vincent, Adrien F verfasserin aut Analytical Macrospin Modeling of the Stochastic Switching Time of Spin-Transfer Torque Devices 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier Owing to their nonvolatility, outstanding endurance, high write and read speeds, and CMOS process compatibility, spin-transfer torque magnetoresistive memories (MRAMs) are prime candidates for innovative memory applications. However, the switching delay of their core components-the magnetic tunnel junctions (MTJs)-is a stochastic quantity. To account for this in electronic design, only partial models (working in extreme regimes) are available. In this paper, we propose an analytical model for the stochastic switching delay of a current-driven MTJ, with in-plane magnetization, that agrees with physical simulations, from low- to high-current regimes through intermediate regime. We performed physical macrospin simulations of MTJs for a wide range of current. We developed an analytical model for the mean switching delay that fits those simulations results, and smoothly connects well-accepted models for the extreme low and extreme high currents limits. In addition, a probability distribution in agreement with our simulations results is proposed, leading to a full model of the stochastic switching delay. An example for the application of the model is proposed. Our analytical model can help to evaluate the error rate in MRAM designs, and allow designing innovative electronic circuits that exploit the intrinsic stochastic behavior of MTJs as a beneficial feature. MRAM simulation Stochastic processes spin-transfer torque magnetoresistive memories magnetic tunnel junctions in-plane magnetization MRAM devices probability physical macrospin simulations magnetic memories Switches stochastic switching delay CMOS process compatibility Mathematical model Delays current-driven MTJ intrinsic stochastic behavior mean switching magnetisation Current density partial models modeling Magnetic devices error rate Analytical models magnetic tunnelling Magnetic tunneling probability distribution Computer memory Random access memory Design Models Magnetoresistance Analysis Magnetic tunnel junctions Distribution (Probability theory) Stochastic analysis Complementary metal oxide semiconductors Design and construction Usage Locatelli, Nicolas oth Klein, Jacques-Olivier oth Zhao, Weisheng S oth Galdin-Retailleau, Sylvie oth Querlioz, Damien oth Enthalten in IEEE transactions on electron devices New York, NY : IEEE, 1963 62(2015), 1, Seite 164-170 (DE-627)129602922 (DE-600)241634-7 (DE-576)015096734 0018-9383 nnns volume:62 year:2015 number:1 pages:164-170 http://dx.doi.org/10.1109/TED.2014.2372475 Volltext http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6981938 http://search.proquest.com/docview/1640794838 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_170 GBV_ILN_2004 GBV_ILN_4313 GBV_ILN_4314 AR 62 2015 1 164-170
allfields_unstemmed	10.1109/TED.2014.2372475 doi PQ20160617 (DE-627)OLC1967766029 (DE-599)GBVOLC1967766029 (PRQ)c2574-c32dbd5c0078f165ffbc2eb01262c23a945176fb4219fe51c786a432ea81282c0 (KEY)0079428720150000062000100164analyticalmacrospinmodelingofthestochasticswitchin DE-627 ger DE-627 rakwb eng 620 DNB Vincent, Adrien F verfasserin aut Analytical Macrospin Modeling of the Stochastic Switching Time of Spin-Transfer Torque Devices 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier Owing to their nonvolatility, outstanding endurance, high write and read speeds, and CMOS process compatibility, spin-transfer torque magnetoresistive memories (MRAMs) are prime candidates for innovative memory applications. However, the switching delay of their core components-the magnetic tunnel junctions (MTJs)-is a stochastic quantity. To account for this in electronic design, only partial models (working in extreme regimes) are available. In this paper, we propose an analytical model for the stochastic switching delay of a current-driven MTJ, with in-plane magnetization, that agrees with physical simulations, from low- to high-current regimes through intermediate regime. We performed physical macrospin simulations of MTJs for a wide range of current. We developed an analytical model for the mean switching delay that fits those simulations results, and smoothly connects well-accepted models for the extreme low and extreme high currents limits. In addition, a probability distribution in agreement with our simulations results is proposed, leading to a full model of the stochastic switching delay. An example for the application of the model is proposed. Our analytical model can help to evaluate the error rate in MRAM designs, and allow designing innovative electronic circuits that exploit the intrinsic stochastic behavior of MTJs as a beneficial feature. MRAM simulation Stochastic processes spin-transfer torque magnetoresistive memories magnetic tunnel junctions in-plane magnetization MRAM devices probability physical macrospin simulations magnetic memories Switches stochastic switching delay CMOS process compatibility Mathematical model Delays current-driven MTJ intrinsic stochastic behavior mean switching magnetisation Current density partial models modeling Magnetic devices error rate Analytical models magnetic tunnelling Magnetic tunneling probability distribution Computer memory Random access memory Design Models Magnetoresistance Analysis Magnetic tunnel junctions Distribution (Probability theory) Stochastic analysis Complementary metal oxide semiconductors Design and construction Usage Locatelli, Nicolas oth Klein, Jacques-Olivier oth Zhao, Weisheng S oth Galdin-Retailleau, Sylvie oth Querlioz, Damien oth Enthalten in IEEE transactions on electron devices New York, NY : IEEE, 1963 62(2015), 1, Seite 164-170 (DE-627)129602922 (DE-600)241634-7 (DE-576)015096734 0018-9383 nnns volume:62 year:2015 number:1 pages:164-170 http://dx.doi.org/10.1109/TED.2014.2372475 Volltext http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6981938 http://search.proquest.com/docview/1640794838 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_170 GBV_ILN_2004 GBV_ILN_4313 GBV_ILN_4314 AR 62 2015 1 164-170
allfieldsGer	10.1109/TED.2014.2372475 doi PQ20160617 (DE-627)OLC1967766029 (DE-599)GBVOLC1967766029 (PRQ)c2574-c32dbd5c0078f165ffbc2eb01262c23a945176fb4219fe51c786a432ea81282c0 (KEY)0079428720150000062000100164analyticalmacrospinmodelingofthestochasticswitchin DE-627 ger DE-627 rakwb eng 620 DNB Vincent, Adrien F verfasserin aut Analytical Macrospin Modeling of the Stochastic Switching Time of Spin-Transfer Torque Devices 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier Owing to their nonvolatility, outstanding endurance, high write and read speeds, and CMOS process compatibility, spin-transfer torque magnetoresistive memories (MRAMs) are prime candidates for innovative memory applications. However, the switching delay of their core components-the magnetic tunnel junctions (MTJs)-is a stochastic quantity. To account for this in electronic design, only partial models (working in extreme regimes) are available. In this paper, we propose an analytical model for the stochastic switching delay of a current-driven MTJ, with in-plane magnetization, that agrees with physical simulations, from low- to high-current regimes through intermediate regime. We performed physical macrospin simulations of MTJs for a wide range of current. We developed an analytical model for the mean switching delay that fits those simulations results, and smoothly connects well-accepted models for the extreme low and extreme high currents limits. In addition, a probability distribution in agreement with our simulations results is proposed, leading to a full model of the stochastic switching delay. An example for the application of the model is proposed. Our analytical model can help to evaluate the error rate in MRAM designs, and allow designing innovative electronic circuits that exploit the intrinsic stochastic behavior of MTJs as a beneficial feature. MRAM simulation Stochastic processes spin-transfer torque magnetoresistive memories magnetic tunnel junctions in-plane magnetization MRAM devices probability physical macrospin simulations magnetic memories Switches stochastic switching delay CMOS process compatibility Mathematical model Delays current-driven MTJ intrinsic stochastic behavior mean switching magnetisation Current density partial models modeling Magnetic devices error rate Analytical models magnetic tunnelling Magnetic tunneling probability distribution Computer memory Random access memory Design Models Magnetoresistance Analysis Magnetic tunnel junctions Distribution (Probability theory) Stochastic analysis Complementary metal oxide semiconductors Design and construction Usage Locatelli, Nicolas oth Klein, Jacques-Olivier oth Zhao, Weisheng S oth Galdin-Retailleau, Sylvie oth Querlioz, Damien oth Enthalten in IEEE transactions on electron devices New York, NY : IEEE, 1963 62(2015), 1, Seite 164-170 (DE-627)129602922 (DE-600)241634-7 (DE-576)015096734 0018-9383 nnns volume:62 year:2015 number:1 pages:164-170 http://dx.doi.org/10.1109/TED.2014.2372475 Volltext http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6981938 http://search.proquest.com/docview/1640794838 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_170 GBV_ILN_2004 GBV_ILN_4313 GBV_ILN_4314 AR 62 2015 1 164-170
allfieldsSound	10.1109/TED.2014.2372475 doi PQ20160617 (DE-627)OLC1967766029 (DE-599)GBVOLC1967766029 (PRQ)c2574-c32dbd5c0078f165ffbc2eb01262c23a945176fb4219fe51c786a432ea81282c0 (KEY)0079428720150000062000100164analyticalmacrospinmodelingofthestochasticswitchin DE-627 ger DE-627 rakwb eng 620 DNB Vincent, Adrien F verfasserin aut Analytical Macrospin Modeling of the Stochastic Switching Time of Spin-Transfer Torque Devices 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier Owing to their nonvolatility, outstanding endurance, high write and read speeds, and CMOS process compatibility, spin-transfer torque magnetoresistive memories (MRAMs) are prime candidates for innovative memory applications. However, the switching delay of their core components-the magnetic tunnel junctions (MTJs)-is a stochastic quantity. To account for this in electronic design, only partial models (working in extreme regimes) are available. In this paper, we propose an analytical model for the stochastic switching delay of a current-driven MTJ, with in-plane magnetization, that agrees with physical simulations, from low- to high-current regimes through intermediate regime. We performed physical macrospin simulations of MTJs for a wide range of current. We developed an analytical model for the mean switching delay that fits those simulations results, and smoothly connects well-accepted models for the extreme low and extreme high currents limits. In addition, a probability distribution in agreement with our simulations results is proposed, leading to a full model of the stochastic switching delay. An example for the application of the model is proposed. Our analytical model can help to evaluate the error rate in MRAM designs, and allow designing innovative electronic circuits that exploit the intrinsic stochastic behavior of MTJs as a beneficial feature. MRAM simulation Stochastic processes spin-transfer torque magnetoresistive memories magnetic tunnel junctions in-plane magnetization MRAM devices probability physical macrospin simulations magnetic memories Switches stochastic switching delay CMOS process compatibility Mathematical model Delays current-driven MTJ intrinsic stochastic behavior mean switching magnetisation Current density partial models modeling Magnetic devices error rate Analytical models magnetic tunnelling Magnetic tunneling probability distribution Computer memory Random access memory Design Models Magnetoresistance Analysis Magnetic tunnel junctions Distribution (Probability theory) Stochastic analysis Complementary metal oxide semiconductors Design and construction Usage Locatelli, Nicolas oth Klein, Jacques-Olivier oth Zhao, Weisheng S oth Galdin-Retailleau, Sylvie oth Querlioz, Damien oth Enthalten in IEEE transactions on electron devices New York, NY : IEEE, 1963 62(2015), 1, Seite 164-170 (DE-627)129602922 (DE-600)241634-7 (DE-576)015096734 0018-9383 nnns volume:62 year:2015 number:1 pages:164-170 http://dx.doi.org/10.1109/TED.2014.2372475 Volltext http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6981938 http://search.proquest.com/docview/1640794838 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_170 GBV_ILN_2004 GBV_ILN_4313 GBV_ILN_4314 AR 62 2015 1 164-170
language	English
source	Enthalten in IEEE transactions on electron devices 62(2015), 1, Seite 164-170 volume:62 year:2015 number:1 pages:164-170
sourceStr	Enthalten in IEEE transactions on electron devices 62(2015), 1, Seite 164-170 volume:62 year:2015 number:1 pages:164-170
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	MRAM simulation Stochastic processes spin-transfer torque magnetoresistive memories magnetic tunnel junctions in-plane magnetization MRAM devices probability physical macrospin simulations magnetic memories Switches stochastic switching delay CMOS process compatibility Mathematical model Delays current-driven MTJ intrinsic stochastic behavior mean switching magnetisation Current density partial models modeling Magnetic devices error rate Analytical models magnetic tunnelling Magnetic tunneling probability distribution Computer memory Random access memory Design Models Magnetoresistance Analysis Magnetic tunnel junctions Distribution (Probability theory) Stochastic analysis Complementary metal oxide semiconductors Design and construction Usage
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authorswithroles_txt_mv	Vincent, Adrien F @@aut@@ Locatelli, Nicolas @@oth@@ Klein, Jacques-Olivier @@oth@@ Zhao, Weisheng S @@oth@@ Galdin-Retailleau, Sylvie @@oth@@ Querlioz, Damien @@oth@@
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author	Vincent, Adrien F
spellingShingle	Vincent, Adrien F ddc 620 misc MRAM misc simulation misc Stochastic processes misc spin-transfer torque magnetoresistive memories misc magnetic tunnel junctions misc in-plane magnetization misc MRAM devices misc probability misc physical macrospin simulations misc magnetic memories misc Switches misc stochastic switching delay misc CMOS process compatibility misc Mathematical model misc Delays misc current-driven MTJ misc intrinsic stochastic behavior misc mean switching misc magnetisation misc Current density misc partial models misc modeling misc Magnetic devices misc error rate misc Analytical models misc magnetic tunnelling misc Magnetic tunneling misc probability distribution misc Computer memory misc Random access memory misc Design misc Models misc Magnetoresistance misc Analysis misc Magnetic tunnel junctions misc Distribution (Probability theory) misc Stochastic analysis misc Complementary metal oxide semiconductors misc Design and construction misc Usage Analytical Macrospin Modeling of the Stochastic Switching Time of Spin-Transfer Torque Devices
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topic_title	620 DNB Analytical Macrospin Modeling of the Stochastic Switching Time of Spin-Transfer Torque Devices MRAM simulation Stochastic processes spin-transfer torque magnetoresistive memories magnetic tunnel junctions in-plane magnetization MRAM devices probability physical macrospin simulations magnetic memories Switches stochastic switching delay CMOS process compatibility Mathematical model Delays current-driven MTJ intrinsic stochastic behavior mean switching magnetisation Current density partial models modeling Magnetic devices error rate Analytical models magnetic tunnelling Magnetic tunneling probability distribution Computer memory Random access memory Design Models Magnetoresistance Analysis Magnetic tunnel junctions Distribution (Probability theory) Stochastic analysis Complementary metal oxide semiconductors Design and construction Usage
topic	ddc 620 misc MRAM misc simulation misc Stochastic processes misc spin-transfer torque magnetoresistive memories misc magnetic tunnel junctions misc in-plane magnetization misc MRAM devices misc probability misc physical macrospin simulations misc magnetic memories misc Switches misc stochastic switching delay misc CMOS process compatibility misc Mathematical model misc Delays misc current-driven MTJ misc intrinsic stochastic behavior misc mean switching misc magnetisation misc Current density misc partial models misc modeling misc Magnetic devices misc error rate misc Analytical models misc magnetic tunnelling misc Magnetic tunneling misc probability distribution misc Computer memory misc Random access memory misc Design misc Models misc Magnetoresistance misc Analysis misc Magnetic tunnel junctions misc Distribution (Probability theory) misc Stochastic analysis misc Complementary metal oxide semiconductors misc Design and construction misc Usage
topic_unstemmed	ddc 620 misc MRAM misc simulation misc Stochastic processes misc spin-transfer torque magnetoresistive memories misc magnetic tunnel junctions misc in-plane magnetization misc MRAM devices misc probability misc physical macrospin simulations misc magnetic memories misc Switches misc stochastic switching delay misc CMOS process compatibility misc Mathematical model misc Delays misc current-driven MTJ misc intrinsic stochastic behavior misc mean switching misc magnetisation misc Current density misc partial models misc modeling misc Magnetic devices misc error rate misc Analytical models misc magnetic tunnelling misc Magnetic tunneling misc probability distribution misc Computer memory misc Random access memory misc Design misc Models misc Magnetoresistance misc Analysis misc Magnetic tunnel junctions misc Distribution (Probability theory) misc Stochastic analysis misc Complementary metal oxide semiconductors misc Design and construction misc Usage
topic_browse	ddc 620 misc MRAM misc simulation misc Stochastic processes misc spin-transfer torque magnetoresistive memories misc magnetic tunnel junctions misc in-plane magnetization misc MRAM devices misc probability misc physical macrospin simulations misc magnetic memories misc Switches misc stochastic switching delay misc CMOS process compatibility misc Mathematical model misc Delays misc current-driven MTJ misc intrinsic stochastic behavior misc mean switching misc magnetisation misc Current density misc partial models misc modeling misc Magnetic devices misc error rate misc Analytical models misc magnetic tunnelling misc Magnetic tunneling misc probability distribution misc Computer memory misc Random access memory misc Design misc Models misc Magnetoresistance misc Analysis misc Magnetic tunnel junctions misc Distribution (Probability theory) misc Stochastic analysis misc Complementary metal oxide semiconductors misc Design and construction misc Usage
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title	Analytical Macrospin Modeling of the Stochastic Switching Time of Spin-Transfer Torque Devices
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title_full	Analytical Macrospin Modeling of the Stochastic Switching Time of Spin-Transfer Torque Devices
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title_sort	analytical macrospin modeling of the stochastic switching time of spin-transfer torque devices
title_auth	Analytical Macrospin Modeling of the Stochastic Switching Time of Spin-Transfer Torque Devices
abstract	Owing to their nonvolatility, outstanding endurance, high write and read speeds, and CMOS process compatibility, spin-transfer torque magnetoresistive memories (MRAMs) are prime candidates for innovative memory applications. However, the switching delay of their core components-the magnetic tunnel junctions (MTJs)-is a stochastic quantity. To account for this in electronic design, only partial models (working in extreme regimes) are available. In this paper, we propose an analytical model for the stochastic switching delay of a current-driven MTJ, with in-plane magnetization, that agrees with physical simulations, from low- to high-current regimes through intermediate regime. We performed physical macrospin simulations of MTJs for a wide range of current. We developed an analytical model for the mean switching delay that fits those simulations results, and smoothly connects well-accepted models for the extreme low and extreme high currents limits. In addition, a probability distribution in agreement with our simulations results is proposed, leading to a full model of the stochastic switching delay. An example for the application of the model is proposed. Our analytical model can help to evaluate the error rate in MRAM designs, and allow designing innovative electronic circuits that exploit the intrinsic stochastic behavior of MTJs as a beneficial feature.
abstractGer	Owing to their nonvolatility, outstanding endurance, high write and read speeds, and CMOS process compatibility, spin-transfer torque magnetoresistive memories (MRAMs) are prime candidates for innovative memory applications. However, the switching delay of their core components-the magnetic tunnel junctions (MTJs)-is a stochastic quantity. To account for this in electronic design, only partial models (working in extreme regimes) are available. In this paper, we propose an analytical model for the stochastic switching delay of a current-driven MTJ, with in-plane magnetization, that agrees with physical simulations, from low- to high-current regimes through intermediate regime. We performed physical macrospin simulations of MTJs for a wide range of current. We developed an analytical model for the mean switching delay that fits those simulations results, and smoothly connects well-accepted models for the extreme low and extreme high currents limits. In addition, a probability distribution in agreement with our simulations results is proposed, leading to a full model of the stochastic switching delay. An example for the application of the model is proposed. Our analytical model can help to evaluate the error rate in MRAM designs, and allow designing innovative electronic circuits that exploit the intrinsic stochastic behavior of MTJs as a beneficial feature.
abstract_unstemmed	Owing to their nonvolatility, outstanding endurance, high write and read speeds, and CMOS process compatibility, spin-transfer torque magnetoresistive memories (MRAMs) are prime candidates for innovative memory applications. However, the switching delay of their core components-the magnetic tunnel junctions (MTJs)-is a stochastic quantity. To account for this in electronic design, only partial models (working in extreme regimes) are available. In this paper, we propose an analytical model for the stochastic switching delay of a current-driven MTJ, with in-plane magnetization, that agrees with physical simulations, from low- to high-current regimes through intermediate regime. We performed physical macrospin simulations of MTJs for a wide range of current. We developed an analytical model for the mean switching delay that fits those simulations results, and smoothly connects well-accepted models for the extreme low and extreme high currents limits. In addition, a probability distribution in agreement with our simulations results is proposed, leading to a full model of the stochastic switching delay. An example for the application of the model is proposed. Our analytical model can help to evaluate the error rate in MRAM designs, and allow designing innovative electronic circuits that exploit the intrinsic stochastic behavior of MTJs as a beneficial feature.
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title_short	Analytical Macrospin Modeling of the Stochastic Switching Time of Spin-Transfer Torque Devices
url	http://dx.doi.org/10.1109/TED.2014.2372475 http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6981938 http://search.proquest.com/docview/1640794838
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author2	Locatelli, Nicolas Klein, Jacques-Olivier Zhao, Weisheng S Galdin-Retailleau, Sylvie Querlioz, Damien
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Analytical Macrospin Modeling of the Stochastic Switching Time of Spin-Transfer Torque Devices

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