arXiv subjects

Xuanyao Fong

Publications and source records attributed to Xuanyao Fong.

36 records · Page 2Linked to original sources

arXiv2023

Efficient domain wall motion driven by the out-of-plane spin polarization

Fast domain wall motion in systems with perpendicular magnetization is necessary for many novel applications such as the racetrack memory, domain wall logic devices and artificial synapses. The domain wall speed has been greatly improved after the demonstration of current driven domain wall motion using the spin transfer torque, and later achieved another leap due to the combined effect of spin-Hall effect and Dzyaloshinskii-Moriya interaction (DMI). However, only Neel wall can be effectively driven in the latter system. In this paper, we show that both Bloch and Neel domain walls can be effectively moved by exploiting the field-like torque with the out-of-plane spin polarization, which can be generated in materials with reduced symmetry. In addition, we find that the Neel wall and Bloch walls stabilized by the magnetostatic energy are easily distorted by the spin-orbit torque and they then enter the Walker breakdown region. External magnetic field and DMI are introduced to stabilize the domain wall, and a very high domain wall velocity over 500 m/s is obtained at a moderate current density of $2\times 10^{11} A/m^2$. In principle, this mechanism can be used to drive any types of domain walls. This work thus provides new degrees of freedom in the control of domain wall motion.

cond-mat.mes-hall↗

arXiv2023

Bilayer-skyrmion-based design of neuron and synapse for spiking neural network

Magnetic skyrmion technology is promising for the next-generation spintronics-based memory and neuromorphic computing due to their small size, non-volatility and low depinning current density. However, the Magnus force originating from the skyrmion Hall effect causes the skyrmion to move along a curved trajectory, which may lead to the annihilation of the skyrmion in a nanotrack during current-induced skyrmion motion. Consequently, circuits utilizing skyrmionic motion need to be designed to limit the impact of the skyrmion Hall effect. In this work, we propose a design of an artificial neuron, and a synapse using the bilayer device consisting of two antiferromagnetically exchange coupled ferromagnetic layers, which achieves robustness against the skyrmion Hall effect by nullifying the Magnus force. Using micromagnetic simulations, we show that the bilayer device can work as an artificial neuron and also as a synapse by modifying its uniaxial anisotropy. We also demonstrate that our proposed skyrmionic synapse has an intrinsic property of perfectly linear and symmetric weight update, which is highly desirable for the synapse operation. A spiking neural network implemented using our proposed synapse and neuron was simulated and showed to achieve 96.23\% accuracy in performing classification on the MNIST handwritten digit dataset.

cond-mat.mes-hall↗

arXiv2022

Self-reset schemes for Magnetic domain wall-based neuron

Spintronic artificial spiking neurons are promising due to their ability to closely mimic the leaky integrate-and-fire (LIF) dynamics of the biological LIF spiking neuron. However, the neuron needs to be reset after firing. Few of the spintronic neurons that have been proposed in the literature discuss the reset process in detail. In this article, we discuss the various schemes to achieve this reset in a magnetic domain wall (DW) based spintronic neuron in which the position of the DW represents the membrane potential. In all the spintronic neurons studied, the neuron enters a refractory period and is reset when the DW reaches a particular position. We show that the self-reset operation in the neuron devices consumes energy that can vary from of several pJ to a few fJ, which highlights the importance of the reset strategy in improving the energy efficiency of spintronic artificial spiking neurons.

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arXiv2022

Electrical Tunable Spintronic Neuron with Trainable Activation Function

Spintronic devices have been widely studied for the hardware realization of artificial neurons. The stochastic switching of magnetic tunnel junction driven by the spin torque is commonly used to produce the sigmoid activation function. However, the shape of the activation function in previous studies is fixed during the training of neural network. This restricts the updating of weights and results in a limited performance. In this work, we exploit the physics behind the spin torque induced magnetization switching to enable the dynamic change of the activation function during the training process. Specifically, the pulse width and magnetic anisotropy can be electrically controlled to change the slope of activation function, which enables a faster or slower change of output required by the backpropagation algorithm. This is also similar to the idea of batch normalization that is widely used in the machine learning. Thus, this work demonstrates that the algorithms are no longer limited to the software implementation. They can in fact be realized by the spintronic hardware using a single device. Finally, we show that the accuracy of hand-written digit recognition can be improved from 88% to 91.3% by using these trainable spintronic neurons without introducing additional energy consumption. Our proposals can stimulate the hardware realization of spintronic neural networks.

cs.ET↗

arXiv2022

Design of Spintronics-based Neuronal and Synaptic Devices for Spiking Neural Network Circuits

Topologically stable magnetic skyrmion has a much lower depinning current density that may be useful for memory as well as neuromorphic computing. However, skyrmion-based devices suffer from the Magnus force originating from the skyrmion Hall effect, which may result in unwanted skyrmion annihilation if the magnitude of the driving current gets too large. A design of an artificial neuron and a synapse using a synthetic antiferromagnetically coupled bilayer device, which nullifies the Magnus force, is demonstrated in this work. The leak term in the artificial leaky integrate-and-fire neuron is achieved by engineering the uniaxial anisotropy profile of the neuronal device. The synaptic device has a similar structure as the neuronal device but has a constant uniaxial anisotropy. The synaptic device also has a linear and symmetric weight update, which is a highly desirable trait of an artificial synapse. Neuronal and synaptic devices based on magnetic domain-wall (DW) motion are also studied and compared to skyrmionic devices. Our simulation results show the energy required to perform such operation in DW or skyrmion-based devices is on the order of a few fJ.

cond-mat.mes-hall↗

arXiv2022

CITS: Coherent Ising Tree Search Algorithm Towards Solving Combinatorial Optimization Problems

Simulated annealing (SA) attracts more attention among classical heuristic algorithms because the solution of the combinatorial optimization problem can be naturally mapped to the ground state of the Ising Hamiltonian. However, in practical implementation, the annealing process cannot be arbitrarily slow and hence, it may deviate from the expected stationary Boltzmann distribution and become trapped in a local energy minimum. To overcome this problem, this paper proposes a heuristic search algorithm by expanding search space from a Markov chain to a recursive depth limited tree based on SA, where the parent and child nodes represent the current and future spin states. At each iteration, the algorithm will select the best near-optimal solution within the feasible search space by exploring along the tree in the sense of `look ahead'. Furthermore, motivated by coherent Ising machine (CIM), we relax the discrete representation of spin states to continuous representation with a regularization term and utilize the reduced dynamics of the oscillators to explore the surrounding neighborhood of the selected tree nodes. We tested our algorithm on a representative NP-hard problem (MAX-CUT) to illustrate the effectiveness of this algorithm compared to semi-definite programming (SDP), SA, and simulated CIM. Our results show that above the primal heuristics SA and CIM, our high-level tree search strategy is able to provide solutions within fewer epochs for Ising formulated NP-optimization problems.

cs.AI↗

arXiv2021

A Fokker-Planck Approach for Modeling the Stochastic Phenomena in Magnetic and Resistive Random Access Memory Devices

Embedded non-volatile memory technologies such as resistive random access memory (RRAM) and spin-transfer torque magnetic RAM (STT MRAM) are increasingly being researched for application in neuromorphic computing and hardware accelerators for AI. However, the stochastic write processes in these memory technologies affect their yield and need to be studied alongside process variations, which drastically increase the complexity of yield analysis using the Monte Carlo approach. Therefore, we propose an approach based on the Fokker-Planck equation for modeling the stochastic write processes in STT MRAM and RRAM devices. Moreover, we show that our proposed approach can reproduce the experimental results for both STT-MRAM and RRAM devices.

cond-mat.mes-hall↗

arXiv2021

Connection Pruning for Deep Spiking Neural Networks with On-Chip Learning

Long training time hinders the potential of the deep, large-scale Spiking Neural Network (SNN) with the on-chip learning capability to be realized on the embedded systems hardware. Our work proposes a novel connection pruning approach that can be applied during the on-chip Spike Timing Dependent Plasticity (STDP)-based learning to optimize the learning time and the network connectivity of the deep SNN. We applied our approach to a deep SNN with the Time To First Spike (TTFS) coding and has successfully achieved 2.1x speed-up and 64% energy savings in the on-chip learning and reduced the network connectivity by 92.83%, without incurring any accuracy loss. Moreover, the connectivity reduction results in 2.83x speed-up and 78.24% energy savings in the inference. Evaluation of our proposed approach on the Field Programmable Gate Array (FPGA) platform revealed 0.56% power overhead was needed to implement the pruning algorithm.

cs.NE↗

arXiv2020

Electrical generation and detection of terahertz signal based on spin-wave emission from ferrimagnets

Terahertz (THz) signals, mainly generated by photonic or electronic approaches, are being sought for various applications, whereas the development of magnetic source might be a necessary step to harness the magnetic nature of electromagnetic radiation. We show that the relativistic effect on the current-driven domain-wall motion induces THz spin-wave emission in ferrimagnets. The required current density increases dramatically in materials with strong exchange interaction and rapidly exceeds 1012 A m-2, leading to the device breakdown and thus the lack of experimental evidence. By translating the collective magnetization oscillations into voltage signals, we propose a three-terminal device for the electrical detection of THz spin wave. Through material engineering, wide frequency range from 264 GHz to 1.1 THz and uniform continuous signals with improved output power can be obtained. As a reverse effect, the spin wave generated in this system is able to move ferrimagnetic domain wall. Our work provides guidelines for the experimental verification of THz spin wave, and could stimulate the design of THz spintronic oscillators for wideband applications as well as the all-magnon spintronic devices.

cond-mat.mes-hall↗

arXiv2020

SIMBA: A Skyrmionic In-Memory Binary Neural Network Accelerator

Magnetic skyrmions are emerging as potential candidates for next generation non-volatile memories. In this paper, we propose an in-memory binary neural network (BNN) accelerator based on the non-volatile skyrmionic memory, which we call as SIMBA. SIMBA consumes 26.7 mJ of energy and 2.7 ms of latency when running an inference on a VGG-like BNN. Furthermore, we demonstrate improvements in the performance of SIMBA by optimizing material parameters such as saturation magnetization, anisotropic energy and damping ratio. Finally, we show that the inference accuracy of BNNs is robust against the possible stochastic behavior of SIMBA (88.5% +/- 1%).

cs.ET↗

arXiv2019

Spin-wave mediated interactions for Majority Computation using Skyrmions and Spin-torque Nano-oscillators

Recent progress in all-electrical nucleation, detection and manipulation of magnetic skyrmions has unlocked the tremendous potential of skyrmion-based spintronic devices. Here, we show via micromagnetic simulations that the stable magnetic oscillations of STNO radiate spin waves (SWs) that can be scattered in the presence of skyrmions in the near vicinity. Interference between SWs emitted by the STNO and SWs scattered by the skyrmion gives rise to interesting dynamics that leads to amplification or attenuation of STNO's magnetic oscillations. In the presence of strong Dzyaloshinskii-Moriya interaction (DMI), the amplified magnetic oscillations evolve into a new skyrmion. These interactions between skyrmions and STNOs are found to be identical for both Neel-type and Bloch-type skyrmions, and are not observed between domain walls and STNOs. These findings offer a novel perspective in processing information using single skyrmions and we propose a 3-bit majority gate for logic applications.

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arXiv2018

Spin-torque-induced magnetization dynamics in ferrimagnets based on Landau-Lifshitz-Bloch Equation

A theoretical model based on the Landau-Lifshitz-Bloch equation is developed to study the spin-torque effect in ferrimagnets. Experimental findings, such as the temperature dependence, the peak in spin torque, and the angular-momentum compensation, can be well captured. In contrast to the ferromagnet system, the switching trajectory in ferrimagnets is found to be precession free. The two sublattices are not always collinear, which produces large exchange field affecting the magnetization dynamics. The study of material composition shows the existence of an oscillation region at intermediate current density, induced by the nondeterministic switching. Compared to the Landau-Lifshitz-Gilbert model, our developed model based on the Landau-Lifshitz-Bloch equation enables the systematic study of spin-torque effect and the evaluation of ferrimagnet-based devices.

cond-mat.mtrl-sci↗

arXiv2018

Electric-field-induced Three-terminal pMTJ Switching in the absence of an External Magnetic Field

Since it is undesirable to require an external magnetic field for on-chip memory applications, we investigate the use of a Rashba effective field alternatively for assisting the electric-field-induced switching operation of a three terminal perpendicular magnetic tunnel junction (pMTJ). By conducting macro-spin simulation, we show that a pMTJ with thermal stability of 61 can be switched in 0.5 ns consuming a switching energy of 6 fJ, and the voltage operation margin can be improved to 0.8 ns. Furthermore, the results also demonstrate that a heavy metal system that can provide large field-like torque rather than damping-like torque is favored for the switching.

cond-mat.mes-hall↗

arXiv2017

Theoretical Proposal for Determining Angular Momentum Compensation in Ferrimagnets

This work demonstrates that the magnetization and angular momentum compensation temperature (TMC and TAMC) in ferrimagnets (FiM) can be unambiguously determined by performing two sets of temperature dependent current switching, with the symmetry reverses at TMC and TAMC, respectively. A theoretical model based on the modified Landau-Lifshitz-Bloch equation is developed to systematically study the spin torque effect under different temperatures, and numerical simulations are performed to corroborate our proposal. Furthermore, we demonstrate that the recently reported linear relation between TAMC and TMC can be explained using the Curie-Weiss theory.

cond-mat.mtrl-sci↗

arXiv2016

Yield, Area and Energy Optimization in Stt-MRAMs using failure aware ECC

Spin Transfer Torque MRAMs are attractive due to their non-volatility, high density and zero leakage. However, STT-MRAMs suffer from poor reliability due to shared read and write paths. Additionally, conflicting requirements for data retention and write-ability (both related to the energy barrier height of the magnet) makes design more challenging. Furthermore, the energy barrier height depends on the physical dimensions of the free layer. Any variations in the dimensions of the free layer lead to variations in the energy barrier height. In order to address poor reliability of STT-MRAMs, usage of Error Correcting Codes (ECC) have been proposed. Unlike traditional CMOS memory technologies, ECC is expected to correct both soft and hard errors in STT_MRAMs. To achieve acceptable yield with low write power, stronger ECC is required, resulting in increased number of encoded bits and degraded memory efficiency. In this paper, we propose Failure aware ECC (FaECC), which masks permanent faults while maintaining the same correction capability for soft errors without increased encoded bits. Furthermore, we investigate the impact of process variations on run-time reliability of STT-MRAMs. We provide an analysis on the impact of process variations on the life-time of the free layer and retention failures. In order to analyze the effectiveness of our methodology, we developed a cross-layer simulation framework that consists of device, circuit and array level analysis of STT-MRAM memory arrays. Our results show that using FaECC relaxes the requirements on the energy barrier height, which reduces the write energy and results in smaller access transistor size and memory array area. Keywords: STT-MRAM, reliability, Error Correcting Codes, ECC, magnetic memory

cs.OH↗

arXiv2015

Modeling and Simulation of Spin Transfer Torque Generated at Topological Insulator/Ferromagnetic Heterostructure

Topological Insulator (TI) has recently emerged as an attractive candidate for possible application to spintronic circuits because of its strong spin orbit coupling. TIs are unique materials that have an insulating bulk but conducting surface states due to band inversion and these surface states are protected by time reversal symmetry. In this paper, we propose a physics-based spin dynamics simulation framework for TI/Ferromagnet (TI/FM) bilayer heterostructures that is able to capture the electronic band structure of a TI while calculating the electron and spin transport properties. Our model differs from TI/FM models proposed in the literature in that it is able to account for the 3D band structure of TIs and the effect of exchange coupling and external magnetic field on the band structure. Our proposed approach uses 2D surface Hamiltonian for TIs that includes all necessary features for spin transport calculations so as to properly model the characteristics of a TI/FM heterostructure. Using this Hamiltonian and appropriate parameters, we show that the effect of quantum confinement and exchange coupling are successfully captured in the calculated surface band structure compared with the quantum well band diagram of a 3D TI, and matches well with experimental data reported in the literature. We then show how this calibrated Hamiltonian is used with the self-consistent non equilibrium Green's functions (NEGF) formalism to determine the charge and spin transport in TI/FM bilayer heterostructures. Our calculations agree well with experimental data and capture the unique features of a TI/FM heterostructure such as high spin Hall angle, high spin conductivity etc. Finally, we show how the results obtained from NEGF calculations may be incorporated into the Landau-Lifshitz-Gilbert-Slonczewski (LLGS) formulation to simulate the magnetization dynamics of an FM layer sitting on top of a TI.

cond-mat.mtrl-sci↗

arXiv2014

Spin-Orbit Torque Induced Spike-Timing Dependent Plasticity

Nanoelectronic devices that mimic the functionality of synapses are a crucial requirement for performing cortical simulations of the brain. In this work we propose a ferromagnet-heavy metal heterostructure that employs spin-orbit torque to implement Spike-Timing Dependent Plasticity. The proposed device offers the advantage of decoupled spike transmission and programming current paths, thereby leading to reliable operation during online learning. Possible arrangement of such devices in a crosspoint architecture can pave the way for ultra-dense neural networks. Simulation studies indicate that the device has the potential of achieving pico-Joule level energy consumption (maximum 2 pJ per synaptic event) which is comparable to the energy consumption for synaptic events in biological synapses.

cs.ET↗

arXiv2014

Laser Induced Magnetization Reversal for Detection in Optical Interconnects

Optical interconnect has emerged as the front-runner to replace electrical interconnect especially for off-chip communication. However, a major drawback with optical interconnects is the need for photodetectors and amplifiers at the receiver, implemented usually by direct bandgap semiconductors and analog CMOS circuits, leading to large energy consumption and slow operating time. In this article, we propose a new optical interconnect architecture that uses a magnetic tunnel junction (MTJ) at the receiver side that is switched by femtosecond laser pulses. The state of the MTJ can be sensed using simple digital CMOS latches, resulting in significant improvement in energy consumption. Moreover, magnetization in the MTJ can be switched on the picoseconds time-scale and our design can operate at a speed of 5 Gbits/sec for a single link.

cond-mat.mtrl-sci↗