Detection of the Microwave Emission from a Spin-Torque Oscillator by a Spin Diode

Marković, Danijela; Leroux, Nathan; Mizrahi, Alice; Trastoy, Juan; Cros, Vincent; Bortolotti, Paolo; Martins, Leandro; Jenkins, Alex; Ferreira, Ricardo; Grollier, Julie

doi:10.1103/physrevapplied.13.044050

Cited by 25 publications

(17 citation statements)

References 22 publications

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“…To quantify this effect, we choose parameters extracted from experiments on similar structures as the resonators. We take nonlinear coefficients N=0.1 and Q=1 close to values determined experimentally in studies of spin-torque nano-oscillators [29,[34][35][36]. We chose frequencies close to 200 MHz, parameters 𝛽 = 1.7 × 10 6 C -1 and 𝛾 = 7.1 × 10 7 Hz.W -1/2 according to experimental values from prior work [34].…”

Section: Multiply-and-accumulate Simulations Results Incorporating De...mentioning

confidence: 99%

Radio-Frequency Multiply-and-Accumulate Operations with Spintronic Synapses

et al. 2021

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Exploiting the physics of nanoelectronic devices is a major lead for implementing compact, fast, and energy efficient artificial intelligence. In this work, we propose an original road in this direction, where assemblies of spintronic resonators used as artificial synapses can classify analogue radio-frequency signals directly without digitalization. The resonators convert the radio-frequency input signals into direct voltages through the spin-diode effect. In the process, they multiply the input signals by a synaptic weight, which depends on their resonance frequency. We demonstrate through physical simulations with parameters extracted from experimental devices that frequency-multiplexed assemblies of resonators implement the cornerstone operation of artificial neural networks, the Multiply-And-Accumulate (MAC), directly on microwave inputs. The results show that even with a non-ideal realistic model, the outputs obtained with our architecture remain comparable to that of a traditional MAC operation. Using a conventional machine learning framework augmented with equations describing the physics of spintronic resonators, we train a single layer neural network to classify radio-frequency signals encoding 8x8 pixel handwritten digits pictures. The spintronic neural network recognizes the digits with an accuracy of 99.96 %, equivalent to purely software neural networks. This MAC implementation offers a promising solution for fast, low-power radio-frequency classification applications, and a new building block for spintronic deep neural networks.Presently, the most promising Artificial Intelligence algorithms are based on deep neural networks [10], which contain several layers of artificial neurons, each of them linked by synaptic connections: in each layer of an artificial neural network, the neuron signals are multiplied by synaptic weights, summed and injected into a neuron of the following layer (see Fig. 1a). This elementary operation is called Multiply-And-Accumulate (MAC). In a computer using the von Neumann architecture, weight multiplications and sums are performed by processing units, whereas synaptic weight values are stored in spatially separated memory units. In such architecture, the data flow between the processing and memory units induces a slowdown and excess energy consumption [11] that can be avoided by implementing the MAC operation in hardware, using in situ memory devices emulating neurons and synapses [12][13][14][15][16][17]. Neurons that take dc inputs and convert them to microwave signals have been demonstrated using spintronic nano-oscillators [18][19][20][21][22][23] and CMOS ring oscillators [24,25]. However, to this day there is no demonstration of tunable artificial synapses that directly perform MAC operations on microwave signals.

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Section: Multiply-and-accumulate Simulations Results Incorporating De...mentioning

confidence: 99%

Radio-Frequency Multiply-and-Accumulate Operations with Spintronic Synapses

et al. 2021

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“…30 The recent proof-of-concept of successful communication between a spin-torque nano-oscillator (STNO) and a passive vortex-based resonant STD (Fig. 1(c)) 31 has opened the path for the realization of a spintronic transceiver. The STNO generates microwave signals compatible with the detection band of the STD.…”

Section: Roadmap For Active Std Applicationsmentioning

confidence: 99%

“…The active STD should be used to replace the passive one of Ref. [31], to achieve higher detection sensitivity. 7,32 In general, the advantage of the spintronic transceiver is the compactness, which ensures on-chip integration with a small area occupancy.…”

Section: Roadmap For Active Std Applicationsmentioning

confidence: 99%

Perspectives on spintronic diodes

Finocchio

Tomasello

Fang

et al. 2021

Applied Physics Letters

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Spintronic diodes are emerging as disruptive candidates for impacting several technological applications ranging from the Internet of Things to Artificial Intelligence. In this letter, an overview of the recent achievements on spintronic diodes is briefly presented, underling the major breakthroughs that have led these devices to have the largest sensitivity measured up to date for a diode. For each class of spintronic diodes (passive, active, resonant, non-resonant), we indicate the remaining developments to improve the performances as well as the future directions. We also dedicate the last part of this perspective to new ideas for developing spintronic diodes in multiphysics systems by combining 2-dimensional materials and antiferromagnets.

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“…In particular, spintronic nano-oscillators can receive, transform and emit RF signals [11][12][13] . They have been proposed as RF emitters and receivers for on-chip communication [14][15][16] and can implement key operations for RF signal processing, such as spectral analysis 17,18 . Furthermore, they are promising building blocks for neuromorphic computing 19,20 .…”

Section: Introductionmentioning

confidence: 99%

Hardware realization of the multiply and accumulate operation on radio-frequency signals with magnetic tunnel junctions

Leroux

Mizrahi

Marković

et al. 2021

Neuromorph. Comput. Eng.

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Artificial neural networks are a valuable tool for radio-frequency (RF) signal classification in many applications, but the digitization of analog signals and the use of general purpose hardware non-optimized for training make the process slow and energetically costly. Recent theoretical work has proposed to use nano-devices called magnetic tunnel junctions, which exhibit intrinsic RF dynamics, to implement in hardware the multiply and accumulate (MAC) operation—a key building block of neural networks—directly using analog RF signals. In this article, we experimentally demonstrate that a magnetic tunnel junction can perform a multiplication of RF powers, with tunable positive and negative synaptic weights. Using two magnetic tunnel junctions connected in series, we demonstrate the MAC operation and use it for classification of RF signals. These results open a path to embedded systems capable of analyzing RF signals with neural networks directly after the antenna, at low power cost and high speed.

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Detection of the Microwave Emission from a Spin-Torque Oscillator by a Spin Diode

Cited by 25 publications

References 22 publications

Radio-Frequency Multiply-and-Accumulate Operations with Spintronic Synapses

Radio-Frequency Multiply-and-Accumulate Operations with Spintronic Synapses

Perspectives on spintronic diodes

Hardware realization of the multiply and accumulate operation on radio-frequency signals with magnetic tunnel junctions

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