Spintronics intelligent devices

Cai, Wenlong; Huang, Yan; Zhang, Xueying; Wang, Shihong; Pan, Yuanhao; Yin, Jialiang; Shi, Kewen; Zhao, Weisheng

doi:10.1007/s11433-022-2012-2

Cited by 14 publications

(3 citation statements)

References 296 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…STNOs are highly tunable and can synchronize with other oscillators through spin waves, magnetic fields, or oscillating currents. It makes them ideal for compact and efficient neural networks and reservoir computing (RC) [20] Additionally, STNOs offer strong scalability, with many new efficient structures continually emerging [21] making them suitable for reservoir construction. More importantly, our previous work [22] has indicated that vertically coupled STNOs can produce two distinct states with different responses, akin to the activated and inactive states of neurons.…”

Section: Introductionmentioning

confidence: 99%

Sparse reservoir computing with vertically coupled vortex spin-torque oscillators for time series prediction

Shen,

Xu,

Jin

et al. 2024

Nanotechnology

View full text Add to dashboard Cite

Spin torque nano-oscillators possessing fast nonlinear dynamics and short-term memory functions are potentially able to achieve energy-efficient neuromorphic computing. In this study, we introduce an activation-state controllable spin neuron unit composed of vertically coupled vortex spin torque oscillators and a V-I source circuit is proposed and used to build an energy-efficient sparse reservoir computing system to solve nonlinear dynamic system prediction task. Based on micromagnetic and electronic circuit simulation, the Mackey-Glass chaotic time series and the real motor vibration signal series can be predicted by the reservoir computing system with merely 20 and 100 spin neuron units, respectively. Further study shows that the proposed sparse reservoir system could reduce energy consumption without significantly compromising performance, and a minimal response from inactivated neurons is crucial for maintaining the system's performance. The accuracy and signal processing speed show the potential of the proposed sparse reservoir computing system for high performance and low-energy neuromorphic computing.

show abstract

Section: Introductionmentioning

confidence: 99%

Sparse reservoir computing with vertically coupled vortex spin-torque oscillators for time series prediction

Shen,

Xu,

Jin

et al. 2024

Nanotechnology

View full text Add to dashboard Cite

show abstract

“…Out of the many candidates for neuromorphic computing at the device level, MTJ devices, shown in figure 1(b), are considered as a more promising technology due to their ultrafast dynamics and ultrahigh endurance, as well as their intrinsic randomness, and their ability to imitate the spiketiming dependence plasticity (STDP) and leaky integrate-fire (LIF) characteristics found in synapses and neurons [12][13][14]. Typically, MTJs are pillar structures consisting of a core structure of three thin film layers, each with thicknesses less than 10 nm.…”

Section: Introductionmentioning

confidence: 99%

Tunneling magnetoresistance materials and devices for neuromorphic computing

et al. 2023

View full text Add to dashboard Cite

Artificial intelligence has become indispensable in modern life, but its energy consumption has become a significant concern due to its huge storage and computational demands. Artificial intelligence algorithms are mainly based on deep learning algorithms, relying on the backpropagation of convolutional neural networks or binary neural networks. While these algorithms aim to simulate the learning process of the human brain, their low bio-fidelity and the separation of storage and computing units lead to significant energy consumption. The human brain is a remarkable computing machine with extraordinary capabilities for recognizing and processing complex information while consuming very low power. Tunneling magnetoresistance (TMR)-based devices, namely magnetic tunnel junctions (MTJs), have great advantages in simulating the behavior of biological synapses and neurons. This is not only because MTJs can simulate biological behavior such as Spike-Timing Dependent Plasticity(STDP) and Leaky Integrate-Fire(LIF), but also because MTJs have intrinsic stochastic and oscillatory properties. These characteristics improve MTJs' bio-fidelity and reduce their power consumption. MTJs also possess advantages such as ultrafast dynamics and non-volatile properties, making them widely utilized in the field of neuromorphic computing in recent years. We conducted a comprehensive review of the development history and underlying principles of TMR, including a detailed introduction to the material and magnetic properties of MTJs and their temperature dependence. We also explored various writing methods of MTJs and their potential applications. Furthermore, we provided a thorough analysis of the characteristics and potential applications of different types of MTJs for neuromorphic computing. TMR-based devices have demonstrated promising potential for broad application in neuromorphic computing, particularly in the development of spiking neural networks. Their ability to perform on-chip learning with ultra-low power consumption makes them an exciting prospect for future advances in the era of the Internet of Things.

show abstract

“…One of the key applications of spintronics is magnetic random-access memory (MRAM), which has attracted widespread research interest and found commercial use particularly in embedded systems, due to its advantages of low power consumption, high endurance, and high integrability compared with other nonvolatile memory technologies. − With logic operations integrated into MRAM arrays, in-memory computing that features great potential to fundamentally break through the Neumann bottleneck can be achieved, fitting well in future big data processes, artificial intelligence, Internet of Things, and edge computing. − The main operation principles of current MRAM can be concisely categorized into information storage, electrical reading, and electrical writing in magnetic tunnel junction devices, achieved through the orientation of magnetic moment, tunnel magnetoresistance (TMR), − and spin-transfer torque/spin–orbit torque (SOT), − respectively. Nevertheless, challenges such as the presence of ferromagnetic (FM) net moment, stray fields, and gigahertz intrinsic frequency make data in MRAM easily erased under magnetic disturbance, as well as limit FM-MARM toward higher integration density and faster operation speed. , Antiferromagnetic (AFM) materials, characterized by their absence of net moment, zero stray field, and terahertz dynamics, naturally present prospects for solving these problems, gaining increasing attention as building blocks of high-performance AFM-MRAM for constructing logic-in-memory. − Similar to FM-MRAM, AFM-MRAM also requires achieving electrical write and electrical readout of the information state stored by the AFM moment within the AFM tunnel junction.…”

mentioning

confidence: 99%

Electrical-Controllable Antiferromagnet-Based Tunnel Junction

Han,

Luo,

et al. 2024

Nano Lett.

View full text Add to dashboard Cite

An electrical-controllable antiferromagnet tunnel junction is a key goal in spintronics, holding immense promise for ultradense and ultrastable antiferromagnetic memory with high processing speed for modern information technology. Here, we have advanced toward this goal by achieving an electrical-controllable antiferromagnet-based tunnel junction of Pt/Co/Pt/Co/IrMn/MgO/Pt. The exchange coupling between antiferromagnetic IrMn and Co/Pt perpendicular magnetic multilayers results in the formation of an interfacial exchange bias and exchange spring in IrMn. Encoding information states “0” and “1” is realized through the exchange spring in IrMn, which can be electrically written by spin–orbit torque switching with high cyclability and electrically read by antiferromagnetic tunneling anisotropic magnetoresistance. Combining spin–orbit torque switching of both exchange spring and exchange bias, a 16 Boolean logic operation is successfully demonstrated. With both memory and logic functionalities integrated into our electrically controllable antiferromagnetic-based tunnel junction, we chart the course toward high-performance antiferromagnetic logic-in-memory.

show abstract

Spintronics intelligent devices

Cited by 14 publications

References 296 publications

Sparse reservoir computing with vertically coupled vortex spin-torque oscillators for time series prediction

Sparse reservoir computing with vertically coupled vortex spin-torque oscillators for time series prediction

Tunneling magnetoresistance materials and devices for neuromorphic computing

Electrical-Controllable Antiferromagnet-Based Tunnel Junction

Contact Info

Product

Resources

About