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Appl. Sci. 2017, 7(9), 929; doi:10.3390/app7090929

High Performance MRAM with Spin-Transfer-Torque and Voltage-Controlled Magnetic Anisotropy Effects

1
National ASIC System Engineering Center, Southeast University, Nanjing 210096, China
2
Département Communications et Electronique, Télécom ParisTech, Université Paris-Saclay, 75013 Paris, France
3
Fert Beijing Institute, Beijing Advanced Innovation Center for Big Data and Brain Computing and School of Electronic and Information Engineering, Beihang Univeristy, Beijing 100191, China
*
Authors to whom correspondence should be addressed.
Received: 14 August 2017 / Revised: 3 September 2017 / Accepted: 7 September 2017 / Published: 11 September 2017
(This article belongs to the Section Computer Science and Electrical Engineering)
View Full-Text   |   Download PDF [4164 KB, uploaded 11 September 2017]   |  

Abstract

The Internet of Things (IoTs) relies on efficient node memories to process data among sensors, cloud and RF front-end. Both mainstream and emerging memories have been developed to achieve this energy efficiency target. Spin transfer torque magnetic tunnel junction (STT-MTJ)-based nonvolatile memory (NVM) has demonstrated great performance in terms of zero standby power, switching power efficiency, infinite endurance and high density. However, it still has a big performance gap; e.g., high dynamic write energy, large latency, yield and reliability. Recently, voltage-controlled magnetic anisotropy (VCMA) has been introduced to achieve improved energy-delay efficiency and robust non-volatile writing control with an electric field or a switching voltage. VCMA-MTJ-based MRAM could be a promising candidate in IoT node memory for high-performance, ultra-low power consumption targets. View Full-Text
Keywords: STT-MRAM; VCMA-MRAM; ultra-low power; reliability STT-MRAM; VCMA-MRAM; ultra-low power; reliability
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Cai, H.; Kang, W.; Wang, Y.; Naviner, L.A.D.B.; Yang, J.; Zhao, W. High Performance MRAM with Spin-Transfer-Torque and Voltage-Controlled Magnetic Anisotropy Effects. Appl. Sci. 2017, 7, 929.

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