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Journal articles on the topic 'Stochastic Magnetic Tunnel Junctions'

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Author: Grafiati

Published: 10 December 2022

Last updated: 28 January 2023

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1

Borders, William A., Ahmed Z. Pervaiz, Shunsuke Fukami, Kerem Y. Camsari, Hideo Ohno, and Supriyo Datta. "Integer factorization using stochastic magnetic tunnel junctions." Nature 573, no. 7774 (September 18, 2019): 390–93. http://dx.doi.org/10.1038/s41586-019-1557-9.

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2

Fukushima, Akio, Kay Yakushiji, Hitoshi Kubota, Hiroshi Imamura, and Shinji Yuasa. "Development of “spin dice” — A Scalable Random Number Generator Based on Spin-Torque Switching." SPIN 09, no. 03 (May 20, 2019): 1940009. http://dx.doi.org/10.1142/s2010324719400095.

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We have developed a random-number-generator (RNG) named “spin dice,” which employs the stochastic nature of spin-torque switching (STS) in a magnetic tunnel junction. The principle of the idea is that the switching probability first tuned around 0.5 is varied linearly with the applied current. After that, the switching results are converted into binary random numbers. We fabricated several types of “spin dice” by combining magnetic tunnel junctions and single-board microcomputer, and achieved generation speed of random numbers up to several hundred kbit/sec. Because STS is scalable and magnetic tunnel junctions have compatibility to semiconductor fabrication process, “spin dice” can be considered as a promising candidate for truly random-number-generator (TRNG) for security applications.

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3

Safranski, Christopher, Jan Kaiser, Philip Trouilloud, Pouya Hashemi, Guohan Hu, and Jonathan Z. Sun. "Demonstration of Nanosecond Operation in Stochastic Magnetic Tunnel Junctions." Nano Letters 21, no. 5 (February 25, 2021): 2040–45. http://dx.doi.org/10.1021/acs.nanolett.0c04652.

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4

Kobayashi, Keito, William A. Borders, Shun Kanai, Keisuke Hayakawa, Hideo Ohno, and Shunsuke Fukami. "Sigmoidal curves of stochastic magnetic tunnel junctions with perpendicular easy axis." Applied Physics Letters 119, no. 13 (September 27, 2021): 132406. http://dx.doi.org/10.1063/5.0065919.

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5

Lv, Wenxing, Jialin Cai, Huayao Tu, Like Zhang, Rongxin Li, Zhe Yuan, Giovanni Finocchio, et al. "Stochastic artificial synapses based on nanoscale magnetic tunnel junction for neuromorphic applications." Applied Physics Letters 121, no. 23 (December 5, 2022): 232406. http://dx.doi.org/10.1063/5.0126392.

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Bio-inspired neuromorphic computing has aroused great interest due to its potential to realize on-chip learning with bio-plausibility and energy efficiency. Realizing spike-timing-dependent plasticity (STDP) in synaptic electronics is critical toward bio-inspired neuromorphic computing systems. Here, we report on stochastic artificial synapses based on nanoscale magnetic tunnel junctions that can implement STDP harnessing stochastic magnetization switching. We further demonstrate that both the magnitude and the temporal requirements for STDP can be modulated via engineering the pre- and post-synaptic voltage pulses. Moreover, based on arrays of binary magnetic synapses, unsupervised learning can be realized for neuromorphic computing tasks such as pattern recognition with great computing accuracy and efficiency. Our study suggests a potential route toward on-chip neuromorphic computing systems.

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6

Chakraborty, Indranil, Amogh Agrawal, Akhilesh Jaiswal, Gopalakrishnan Srinivasan, and Kaushik Roy. "In situ unsupervised learning using stochastic switching in magneto-electric magnetic tunnel junctions." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 378, no. 2164 (December 23, 2019): 20190157. http://dx.doi.org/10.1098/rsta.2019.0157.

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Spiking neural networks (SNNs) offer a bio-plausible and potentially power-efficient alternative to conventional deep learning. Although there has been progress towards implementing SNN functionalities in custom CMOS-based hardware using beyond Von Neumann architectures, the power-efficiency of the human brain has remained elusive. This has necessitated investigations of novel material systems which can efficiently mimic the functional units of SNNs, such as neurons and synapses. In this paper, we present a magnetoelectric–magnetic tunnel junction (ME-MTJ) device as a synapse. We arrange these synapses in a crossbar fashion and perform in situ unsupervised learning. We leverage the capacitive nature of write-ports in ME-MTJs, wherein by applying appropriately shaped voltage pulses across the write-port, the ME-MTJ can be switched in a probabilistic manner. We further exploit the sigmoidal switching characteristics of ME-MTJ to tune the synapses to follow the well-known spike timing-dependent plasticity (STDP) rule in a stochastic fashion. Finally, we use the stochastic STDP rule in ME-MTJ synapses to simulate a two-layered SNN to perform image classification tasks on a handwritten digit dataset. Thus, the capacitive write-port and the decoupled-nature of read-write path of ME-MTJs allow us to construct a transistor-less crossbar, suitable for energy-efficient implementation of in situ learning in SNNs. This article is part of the theme issue ‘Harmonizing energy-autonomous computing and intelligence’.

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7

Velarde, Humberto Inzunza, Jheel Nagaria, Zihan Yin, Ajey Jacob, and Akhilesh Jaiswal. "Intrinsic Spike-Timing-Dependent Plasticity in Stochastic Magnetic Tunnel Junctions Mediated by Heat Dynamics." IEEE Magnetics Letters 12 (2021): 1–5. http://dx.doi.org/10.1109/lmag.2021.3136154.

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8

Debashis, Punyashloka, Hai Li, Dmitri Nikonov, and Ian Young. "Gaussian Random Number Generator With Reconfigurable Mean and Variance Using Stochastic Magnetic Tunnel Junctions." IEEE Magnetics Letters 13 (2022): 1–5. http://dx.doi.org/10.1109/lmag.2022.3152991.

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9

Lee, Albert, Di Wu, and Kang L. Wang. "Torque Optimization for Voltage-Controlled Magnetic Tunnel Junctions as Memory and Stochastic Signal Generators." IEEE Magnetics Letters 10 (2019): 1–4. http://dx.doi.org/10.1109/lmag.2019.2944805.

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10

Algarín, J. M., B. Ramaswamy, Y. J. Chen, I. N. Weinberg, I. N. Krivorotov, J. A. Katine, B. Shapiro, and E. Waks. "High rectification sensitivity of radiofrequency signal through adiabatic stochastic resonance in nanoscale magnetic tunnel junctions." Applied Physics Letters 115, no. 19 (November 4, 2019): 192402. http://dx.doi.org/10.1063/1.5123466.

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11

Kim, Taeyueb, HeeGyum Park, Ki-Hyuk Han, Young-Jun Nah, Hyun Cheol Koo, Byoung-Chul Min, Seokmin Hong, and OukJae Lee. "Demonstration of in-plane magnetized stochastic magnetic tunnel junction for binary stochastic neuron." AIP Advances 12, no. 7 (July 1, 2022): 075104. http://dx.doi.org/10.1063/5.0090577.

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A binary stochastic neuron (BSN) or a probabilistic bit (p-bit) randomly fluctuates between digitized “0” and “1” with a controllable functionality of time-averaged value. Such an unconventional bit is the most essential building block for the recently proposed stochastic neural networks and probabilistic computing. Here, we experimentally implement a magnetic tunnel junction (MTJ) for BSN, with relaxation times on the order of tens of milliseconds that can be modulated by a current-induced spin-transfer torque. The NIST Statistical Test Suite (800-22a) is used to verify true random number generation by the BSN-MTJ device. Our results suggest the possibility of using the artificial BSN MTJ device in neuromorphic applications as well as in a recently proposed probabilistic computing.

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12

Radfar, Mohsen, Karthik Yogendra, and Kaushik Roy. "Stochastic Quantization Using Magnetic Tunnel Junction Devices: A Simulation Study." IEEE Transactions on Magnetics 53, no. 3 (March 2017): 1–6. http://dx.doi.org/10.1109/tmag.2016.2635631.

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13

Zhang, Yue, Weisheng Zhao, Guillaume Prenat, Thibaut Devolder, Jacques-Olivier Klein, Claude Chappert, Bernard Dieny, and Dafine Ravelosona. "Electrical Modeling of Stochastic Spin Transfer Torque Writing in Magnetic Tunnel Junctions for Memory and Logic Applications." IEEE Transactions on Magnetics 49, no. 7 (July 2013): 4375–78. http://dx.doi.org/10.1109/tmag.2013.2242257.

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14

Kil, Gyuhyun, Juntae Choi, and Yunheub Song. "Macro model for stochastic behavior of resistance distribution of magnetic tunnel junction." Japanese Journal of Applied Physics 54, no. 4S (March 26, 2015): 04DD12. http://dx.doi.org/10.7567/jjap.54.04dd12.

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15

Onizawa, Naoya, Daisaku Katagiri, Warren J. Gross, and Takahiro Hanyu. "Analog-to-Stochastic Converter Using Magnetic Tunnel Junction Devices for Vision Chips." IEEE Transactions on Nanotechnology 15, no. 5 (September 2016): 705–14. http://dx.doi.org/10.1109/tnano.2015.2511151.

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16

Wang, Y., Y. Zhang, E. Y. Deng, J. O. Klein, L. A. B. Naviner, and W. S. Zhao. "Compact model of magnetic tunnel junction with stochastic spin transfer torque switching for reliability analyses." Microelectronics Reliability 54, no. 9-10 (September 2014): 1774–78. http://dx.doi.org/10.1016/j.microrel.2014.07.019.

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17

Lone, Aijaz H., S. Amara, and H. Fariborzi. "Magnetic tunnel junction based implementation of spike time dependent plasticity learning for pattern recognition." Neuromorphic Computing and Engineering 2, no. 2 (March 25, 2022): 024003. http://dx.doi.org/10.1088/2634-4386/ac57a2.

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Abstract We present a magnetic tunnel junction (MTJ) based implementation of the spike time-dependent (STDP) learning for pattern recognition applications. The proposed hybrid scheme utilizes the spin–orbit torque (SOT) driven neuromorphic device-circuit co-design to demonstrate the Hebbian learning algorithm. The circuit implementation involves the (MTJ) device structure, with the domain wall motion in the free layer, acting as an artificial synapse. The post-spiking neuron behaviour is implemented using a low barrier MTJ. In both synapse and neuron, the switching is driven by the SOTs generated by the spin Hall effect in the heavy metal. A coupled model for the spin transport and switching characteristics in both devices is developed by adopting a modular approach to spintronics. The thermal effects in the synapse and neuron result in a stochastic but tuneable domain wall motion in the synapse and a superparamagnetic behaviour of in neuron MTJ. Using the device model, we study the dimensional parameter dependence of the switching delay and current to optimize the device dimensions. The optimized parameters corresponding to synapse and neuron are considered for the implementation of the Hebbian learning algorithm. Furthermore, cross-point architecture and STDP-based weight modulation scheme is used to demonstrate the pattern recognition capabilities by the proposed neuromorphic circuit.

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18

Lone, Aijaz H., S. Amara, and H. Fariborzi. "Voltage-Controlled Domain Wall Motion-Based Neuron and Stochastic Magnetic Tunnel Junction Synapse for Neuromorphic Computing Applications." IEEE Journal on Exploratory Solid-State Computational Devices and Circuits 8, no. 1 (June 2022): 1–9. http://dx.doi.org/10.1109/jxcdc.2021.3138038.

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19

Nisar, Arshid, Farooq A. Khanday, and Brajesh Kumar Kaushik. "Implementation of an efficient magnetic tunnel junction-based stochastic neural network with application to iris data classification." Nanotechnology 31, no. 50 (October 6, 2020): 504001. http://dx.doi.org/10.1088/1361-6528/abadc4.

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20

Choi, Juntae, and Yunheub Song. "Stochastic macromodel of magnetic tunnel junction resistance variation and critical current dependence on resistance variation for SPICE simulation." Japanese Journal of Applied Physics 56, no. 4S (February 23, 2017): 04CN03. http://dx.doi.org/10.7567/jjap.56.04cn03.

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21

Chen, Wenhan, Haodi Tang, Yu Wang, Xianwu Hu, Yuming Lin, Tai Min, and Yufeng Xie. "E-Spin: A Stochastic Ising Spin Based on Electrically-Controlled MTJ for Constructing Large-Scale Ising Annealing Systems." Micromachines 14, no. 2 (January 19, 2023): 258. http://dx.doi.org/10.3390/mi14020258.

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With its unique computer paradigm, the Ising annealing machine has become an emerging research direction. The Ising annealing system is highly effective at addressing combinatorial optimization (CO) problems that are difficult for conventional computers to tackle. However, Ising spins, which comprise the Ising system, are difficult to implement in high-performance physical circuits. We propose a novel type of Ising spin based on an electrically-controlled magnetic tunnel junction (MTJ). Electrical operation imparts true randomness, great stability, precise control, compact size, and easy integration to the MTJ-based spin. In addition, simulations demonstrate that the frequency of electrically-controlled stochastic Ising spin (E-spin) is 50 times that of the thermal disturbance MTJ-based spin (p-bit). To develop a large-scale Ising annealing system, up to 64 E-spins are implemented. Our Ising annealing system demonstrates factorization of integers up to 264 with a temporal complexity of around O(n). The proposed E-spin shows superiority in constructing large-scale Ising annealing systems and solving CO problems.

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22

Zhu, Jian-Gang (Jimmy), and Chando Park. "Magnetic tunnel junctions." Materials Today 9, no. 11 (November 2006): 36–45. http://dx.doi.org/10.1016/s1369-7021(06)71693-5.

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23

Bai, Haili, and Enyong Jiang. "Magnetic tunnel junctions (MTJs)." Chinese Science Bulletin 46, no. 9 (May 2001): 709–16. http://dx.doi.org/10.1007/bf03187205.

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24

Li, X. W., Yu Lu, G. Q. Gong, Gang Xiao, A. Gupta, P. Lecoeur, J. Z. Sun, Y. Y. Wang, and V. P. Dravid. "Epitaxial La0.67Sr0.33MnO3 magnetic tunnel junctions." Journal of Applied Physics 81, no. 8 (April 15, 1997): 5509–11. http://dx.doi.org/10.1063/1.364585.

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25

Gallagher, W. J., S. S. P. Parkin, Yu Lu, X. P. Bian, A. Marley, K. P. Roche, R. A. Altman, et al. "Microstructured magnetic tunnel junctions (invited)." Journal of Applied Physics 81, no. 8 (April 15, 1997): 3741–46. http://dx.doi.org/10.1063/1.364744.

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26

Cobas, Enrique, Adam L. Friedman, Olaf M. J. van 't Erve, Jeremy T. Robinson, and Berend T. Jonker. "Graphene-Based Magnetic Tunnel Junctions." IEEE Transactions on Magnetics 49, no. 7 (July 2013): 4343–46. http://dx.doi.org/10.1109/tmag.2013.2245107.

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27

Wen, Z. C., H. X. Wei, and X. F. Han. "Patterned nanoring magnetic tunnel junctions." Applied Physics Letters 91, no. 12 (September 17, 2007): 122511. http://dx.doi.org/10.1063/1.2786591.

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28

Kinder, R., L. Bär, G. Rupp, U. K. Klostermann, J. Bangert, G. Bayreuther, and J. Wecker. "Stability of magnetic tunnel junctions." Journal of Magnetism and Magnetic Materials 240, no. 1-3 (February 2002): 314–16. http://dx.doi.org/10.1016/s0304-8853(01)00790-9.

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29

Zhang, Xueying, Wenlong Cai, Xichao Zhang, Zilu Wang, Zhi Li, Yu Zhang, Kaihua Cao, et al. "Skyrmions in Magnetic Tunnel Junctions." ACS Applied Materials & Interfaces 10, no. 19 (April 23, 2018): 16887–92. http://dx.doi.org/10.1021/acsami.8b03812.

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30

Koller, P. H. P., F. W. M. Vanhelmont, R. Coehoorn, and W. J. M. de Jonge. "Photoconductance in magnetic tunnel junctions." IEEE Transactions on Magnetics 38, no. 5 (September 2002): 2712–14. http://dx.doi.org/10.1109/tmag.2002.803171.

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31

Tiusan, C., T. Dimopoulos, K. Ounadjela, M. Hehn, H. A. M. van den Berg, V. da Costa, and Y. Henry. "Tunnel magnetoresistance versus micromagnetism in magnetic tunnel junctions." Journal of Applied Physics 87, no. 9 (May 2000): 4676–78. http://dx.doi.org/10.1063/1.373127.

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32

Apachitei, Geanina, Jonathan J. P. Peters, Ana M. Sanchez, Dong Jik Kim, and Marin Alexe. "Antiferroelectric Tunnel Junctions." Advanced Electronic Materials 3, no. 7 (May 15, 2017): 1700126. http://dx.doi.org/10.1002/aelm.201700126.

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33

Sukegawa, Hiroaki, Nobuki Tezuka, Koichiro Inomata, and Satoshi Sugimoto. "Magnetoresistance of Magnetic Double Tunnel Junctions." Journal of the Japan Institute of Metals 68, no. 2 (2004): 74–77. http://dx.doi.org/10.2320/jinstmet.68.74.

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34

Parker, Matthew. "Magnetic tunnel junctions hit the buffer." Nature Electronics 4, no. 7 (July 2021): 453. http://dx.doi.org/10.1038/s41928-021-00626-5.

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35

Suchaneck, Gunnar, Evgenii Artiukh, Nikolai A. Sobolev, Eugene Telesh, Nikolay Kalanda, Dmitry A. Kiselev, Tatiana S. Ilina, and Gerald Gerlach. "Strontium Ferromolybdate-Based Magnetic Tunnel Junctions." Applied Sciences 12, no. 5 (March 5, 2022): 2717. http://dx.doi.org/10.3390/app12052717.

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Thin-film strontium ferromolybdate is a promising material for applications in room-temperature magnetic tunnel junction devices. These are spin-based, low-power-consuming alternatives to CMOS in non-volatile memories, comparators, analog-to-digital converters, and magnetic sensors. In this work, we consider the main tasks to be solved when creating such devices based on strontium ferromolybdate: (i) selecting an appropriate tunnel barrier material, (ii) determining the role of the interface roughness and its quantification, (iii) determining the influence of the interface dead layer, (iv) establishing appropriate models of the tunnel magnetoresistance, and (v) promoting the low-field magnetoresistance in (111)-oriented thin films. We demonstrate that (i) barrier materials with a lower effective electronegativity than strontium ferromolybdate are beneficial, (ii) diminution of the magnetic offset field (the latter caused by magnetic coupling) requires a wavy surface rather than solely a surface with small roughness, (iii) the interface dead-layer thickness is of the order of 10 nm, (iv) the tunnel magnetoresistance deteriorates due to spin-independent tunneling and magnetically disordered interface layers, and (v) antiphase boundaries along the growth direction promote the negative low-field magnetoresistance by reducing charge carrier scattering in the absence of the field.

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36

Lee, Byung Chan. "Spin Current in Magnetic Tunnel Junctions." Journal of the Korean Physical Society 58, no. 4 (April 15, 2011): 855–58. http://dx.doi.org/10.3938/jkps.58.855.

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37

Rzchowski, M. S., and X. W. Wu. "Bias dependence of magnetic tunnel junctions." Physical Review B 61, no. 9 (March 1, 2000): 5884–87. http://dx.doi.org/10.1103/physrevb.61.5884.

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38

Reiss, G., and D. Meyners. "Logic based on magnetic tunnel junctions." Journal of Physics: Condensed Matter 19, no. 16 (April 5, 2007): 165220. http://dx.doi.org/10.1088/0953-8984/19/16/165220.

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39

Mehrez, H., Jeremy Taylor, Hong Guo, Jian Wang, and Christopher Roland. "Carbon Nanotube Based Magnetic Tunnel Junctions." Physical Review Letters 84, no. 12 (March 20, 2000): 2682–85. http://dx.doi.org/10.1103/physrevlett.84.2682.

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40

Waldron, Derek, Lei Liu, and Hong Guo. "Ab initiosimulation of magnetic tunnel junctions." Nanotechnology 18, no. 42 (September 21, 2007): 424026. http://dx.doi.org/10.1088/0957-4484/18/42/424026.

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41

Baranov, A. M., Yu V. Gulyaev, P. E. Zilberman, A. I. Krikunov, V. V. Kudryavtsev, Yu F. Ogrin, V. P. Sklizkova, et al. "Current hysteresis in magnetic tunnel junctions." Physics of the Solid State 43, no. 6 (June 2001): 1093–96. http://dx.doi.org/10.1134/1.1378150.

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42

Allen, D., R. Schad, G. Zangari, I. Zana, D. Yang, M. C. Tondra, and D. Wang. "Pinhole decoration in magnetic tunnel junctions." Journal of Vacuum Science & Technology A: Vacuum, Surfaces, and Films 18, no. 4 (July 2000): 1830–33. http://dx.doi.org/10.1116/1.582431.

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43

Allen, D., R. Schad, Giovanni Zangari, Iulica Zana, D. Yang, Mark Tondra, and Dexin Wang. "Pinhole imaging in magnetic tunnel junctions." Journal of Applied Physics 87, no. 9 (May 2000): 5188–90. http://dx.doi.org/10.1063/1.373290.

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44

Samant, Mahesh G., and Stuart S. P. Parkin. "Magnetic tunnel junctions—principles and applications." Vacuum 74, no. 3-4 (June 2004): 705–9. http://dx.doi.org/10.1016/j.vacuum.2004.01.053.

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45

Schad, R., D. Allen, Giovanni Zangari, Iulica Zana, D. Yang, Mark Tondra, and Dexin Wang. "Pinhole analysis in magnetic tunnel junctions." Applied Physics Letters 76, no. 5 (January 31, 2000): 607–9. http://dx.doi.org/10.1063/1.125832.

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46

Jiang, Xin, Alex F. Panchula, and Stuart S. P. Parkin. "Magnetic tunnel junctions with ZnSe barriers." Applied Physics Letters 83, no. 25 (December 22, 2003): 5244–46. http://dx.doi.org/10.1063/1.1630160.

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47

Walter, Marvin, Jakob Walowski, Vladyslav Zbarsky, Markus Münzenberg, Markus Schäfers, Daniel Ebke, Günter Reiss, et al. "Seebeck effect in magnetic tunnel junctions." Nature Materials 10, no. 10 (July 24, 2011): 742–46. http://dx.doi.org/10.1038/nmat3076.

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48

Xiang, X. H., T. Zhu, F. Sheng, Z. Zhang, and J. Q. Xiao. "Recent developments in magnetic tunnel junctions." IEEE Transactions on Magnetics 39, no. 5 (September 2003): 2770–75. http://dx.doi.org/10.1109/tmag.2003.815705.

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49

Schad, R., K. Mayen, J. McCord, D. Allen, D. Yang, M. Tondra, and D. Wang. "Interface composition in magnetic tunnel junctions." Journal of Applied Physics 89, no. 11 (June 2001): 6659–61. http://dx.doi.org/10.1063/1.1356710.

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50

Ingvarsson, S., Gang Xiao, R. A. Wanner, P. Trouilloud, Yu Lu, W. J. Gallagher, A. Marley, K. P. Roche, and S. S. P. Parkin. "Electronic noise in magnetic tunnel junctions." Journal of Applied Physics 85, no. 8 (April 15, 1999): 5270–72. http://dx.doi.org/10.1063/1.369851.

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