United States

The Ising machine is an unconventional computing architecture that can be used to solve NP-hard combinatorial optimization problems more efficiently than traditional von Neumann architectures. GHz spin Hall nano-oscillators (SHNOs) are a particularly attractive technology for building fast, energy-efficient, and scalable Ising machines; however, electrical coupling mechanisms which allow for full programmability among different oscillator nodes have not yet been fully demonstrated in such a network. Here, we develop a general analytical framework that can describe injection locking of SHNOs with arbitrary oscillation orbits at both the fundamental frequency and harmonics. With this compact analytical framework, we integrate the SHNO into a Verilog-A device model that can emulate the oscillator’s injection locking behavior in circuit simulations, where the dynamics of coupled oscillator networks are simulated together with conventional electronic components (Fig. 1). While our abstract circuit simulations achieve similar accuracy as full micromagnetic simulations, the device model we developed leads to more than 100 times improvement in simulation efficiency and allows us to evaluate the performance using realistic circuits.&lt;br/&gt;Using circuit-level simulations, we further study the effects of phase noise and scalability in networks of up to hundreds of coupled oscillators to show that the SHNO-based Ising machine can be operated robustly at room temperature. Compared with existing technologies, SHNO networks exhibit orders of magnitude improvement in time, space, and energy efficiency (Table 1). Our results provide analytical tools and design insights that will be useful for the realization of a CMOS-integrated SHNO Ising machine.

![](https://assets.underline.io/uploads/markdown_image/1/image/a0dab57c9a06319f78760697037422fd.jpg)

Fig. 1: (a) Electrical coupling circuit linking two SHNOs in the oscillator-based Ising machine. (b) Phases of oscillators in a 4-node coupled network solving the Ising model in circuit simulations (HSPICE) using our abstract device model and in micromagnetic simulations (MuMax3).

![](https://assets.underline.io/uploads/markdown_image/1/image/56041ec137cf8fc9052b69c276e29915.jpg)

Table 1: Comparison of speed, energy, and space metrics between the SHNO Ising machine and existing technologies. Values are standardized to a 100-node network.

2022 Joint MMM-INTERMAG

Using Machine Based on Electrically Coupled Spin Hall Nano-Oscillators

#### Welcome to the 67th Annual Conference on Magnetism and Magnetic Materials, MMM 2022!

The Conference was held from October 31 to November 4 and offers both in-person and on-demand content.

MMM 2022 is sponsored jointly by AIP Publishing and the IEEE Magnetics Society. Members of the international scientific and engineering communities interested in recent developments in fundamental and applied magnetism are invited to attend and contribute to the technical sessions. The technical program will include invited and contributed papers in oral and poster sessions, invited symposia, a tutorial, and several special sessions. This Conference provides an outstanding opportunity for worldwide participants to meet their colleagues and collaborators and discuss developments in all areas of magnetism research.

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For tickets to the event, please register at the following link: https://magnetism.org/registration

Please register for the MMM 2022 conference to join the event.

MMM 2022

This Conference provides an outstanding opportunity for worldwide participants to meet their colleagues and collaborators and discuss developments in all areas of magnetism research.

The Ising machine is an unconventional computing architecture that can be used to solve NP-hard combinatorial optimization problems more efficiently than traditional von Neumann architectures. GHz spin Hall nano-oscillators (SHNOs) are a particularly attractive technology for building fast, energy-efficient, and scalable Ising machines; however, electrical coupling mechanisms which allow for full programmability among different oscillator nodes have not yet been fully demonstrated in such a network. Here, we develop a general analytical framework that can describe injection locking of SHNOs with arbitrary oscillation orbits at both the fundamental frequency and harmonics. With this compact analytical framework, we integrate the SHNO into a Verilog-A device model that can emulate the oscillator’s injection locking behavior in circuit simulations, where the dynamics of coupled oscillator networks are simulated together with conventional electronic components (Fig. 1). While our abstract circuit simulations achieve similar accuracy as full micromagnetic simulations, the device model we developed leads to more than 100 times improvement in simulation efficiency and allows us to evaluate the performance using realistic circuits. Using circuit-level simulations, we further study the effects of phase noise and scalability in networks of up to hundreds of coupled oscillators to show that the SHNO-based Ising machine can be operated robustly at room temperature. Compared with existing technologies, SHNO networks exhibit orders of magnitude improvement in time, space, and energy efficiency (Table 1). Our results provide analytical tools and design insights that will be useful for the realization of a CMOS-integrated SHNO Ising machine.

![](https://assets.underline.io/uploads/markdown_image/1/image/a0dab57c9a06319f78760697037422fd.jpg)

Fig. 1: (a) Electrical coupling circuit linking two SHNOs in the oscillator-based Ising machine. (b) Phases of oscillators in a 4-node coupled network solving the Ising model in circuit simulations (HSPICE) using our abstract device model and in micromagnetic simulations (MuMax3).

![](https://assets.underline.io/uploads/markdown_image/1/image/56041ec137cf8fc9052b69c276e29915.jpg)

Table 1: Comparison of speed, energy, and space metrics between the SHNO Ising machine and existing technologies. Values are standardized to a 100-node network.

technical paper

The 15th Joint MMM-INTERMAG Conference (2022 Joint) is sponsored jointly by AIP Publishing and the IEEE Magnetics Society. Members of the international scientific and engineering communities interested in recent developments in fundamental and applied magnetism are invited to attend and contribute to the technical sessions. The technical program will include invited and contributed papers in oral and poster sessions, invited symposia, a plenary session, and an evening session, with about 1500 presentations overall. This Conference provides an outstanding opportunity for worldwide participants to meet their colleagues and collaborators and discuss developments in all areas of magnetism research.

The 2022 Joint Conference will offer both in-person and prerecorded on-demand content.

For tickets to the event, please register at the following link: https://magnetism.org/register

Please register for the Joint MMM-Intermag conference to join the event.

The rhythmic spiking behavior observed in biological neurons [1] has inspired oscillator based implementations of neural networks [2]. We theoretically investigate the use of synchronized spin-torque nano-oscillators (STNOs) to create an associative memory encoded with synaptic weights as time delays. STNOs frequency lock to external AC drives if the external drive frequency is close to the natural frequency of the STNO [3] but the relative phase varies with the details. Fig. 1 shows the implementation of a complex Hopfield network in which the relative phases between the synchronized STNO oscillations encode quasi-continuously varying information, such as the pixel values of a grayscale image. The synaptic feedback matrix implements a complex weight matrix by taking the AC output voltages from the STNO array and re-injecting weighted and time-delayed superpositions of AC currents back into the STNO array. The feedback matrix is set using one of several training methods. Then, retrieval of a stored pattern in the associative memory proceeds as shown in Fig. 2. The feedback is disconnected and the individual oscillators are injection locked to an array of external AC sources that encode the query image. A schematic evolution of the relative phases of the individual oscillators, starting from a random state, during this preparation phase is plotted in Fig. 2a. Then, the feedback connections are engaged and the network evolves towards one of the patterns stored in the synaptic weights as shown in Fig. 2b. We design CMOS circuitry to implement the synapses and use a compact model for the STNO dynamics to simulate the circuit with SPICE. These simulations give energy efficiencies and parametric restrictions on the properties of the STNOs and the CMOS circuitry needed for implementing large associative memories.

![](https://assets.underline.io/uploads/markdown_image/1/image/a51918677f3e133fdef9e7b4b0ee3a96.jpg)

Schematic of the complex Hopfield network implemented using STNO neurons

![](https://assets.underline.io/uploads/markdown_image/1/image/a751556a04383cb4f92620da4a94de34.jpg)

Steps involved in a typical pattern retrieval operation. (a) The phases of the oscillators are prepared to encode the query image by driving the oscillators with an array of external inputs. (b) The oscillators evolve towards one of the stored images as the feedback is turned on.



Complex Hopfield Networks Implemented Using Coupled Spin-Torque Oscillator Arrays

Spintronic diodes (STDs) are emerging as a technology for the realization of high-performance microwave detectors [1]. In this paper, we use their rectification properties to perform the analog multiplication between any two numbers. For this purpose, we introduce the concept of degree of rectification (DOR) and we confirm its applicability with micromagnetic and experimental data. The proposed method is suitable for highly integrated implementations and has promising perspectives for neural networks applications [2]. The multiplication can be obtained by measuring the response of a parabolic phenomenon to three well-determined inputs [3][4][5]. Therefore, we analyze the DOR of spintronic diodes, which is the output dc voltage measured for a variable input dc current in injection-locked devices. This Vdc/Idc curve can be well approximated to a parabola as shown in Fig. 1, the experimental data are taken from [6]. The voltage can be approximated by the following equation [6]: Vdc=[(RAP-RP )/4] Iac,MAX cos(φ(Idc)) √p (1) where RAP and RP are the device resistances in the antiparallel and parallel state, respectively, p is the power of the magnetoresistive signal, φ(Idc) is the intrinsic phase shift between the ac current and the magnetoresistive signal, and the cosine can be well approximated with a parable using Taylor’s second-order expansion. Moreover, Eq. 1 can be fully characterized knowing the physical parameters of the device and the values of Idc,MAX, Vdc,MAX (both represented in Fig. 1) and the second derivative of Vdc with respect to Idc evaluated in Idc=Idc,MAX. Fig. 2 shows the application of the presented method for 200 cases with random inputs, the red line shows the ideal result; the correlation coefficient found is r=99.83%. We will show how the proposed method can be used to implement the convolution for neural networks applications with excellent results for image recognition.

![](https://assets.underline.io/uploads/markdown_image/1/image/8be5d570eb2198667334d746191b22a6.jpg)

Fig. 1: Output dc voltage depending on the input dc current (data taken from [4]) for an injection-locked diode (blue circles) and parabolic fit (red line).

![](https://assets.underline.io/uploads/markdown_image/1/image/ef2d86778d75be9a7a2f863250048857.jpg)

Fig. 2: 200 multiplications obtained with the proposed method (black circles) compared with the ideal result (red line).

Analog multiplication implemented with spin-torque diodes for neural networks applications

Perpendicular Spin Transfer Torque Magnetic Random Access Memory (p-STT-MRAM) needs to combine high speed and low critical switching current at technological nodes below 30 nm. Higher STT write efficiency is therefore required. Double magnetic tunnel junctions (DMTJ) stacks have been proposed to achieve this goal [1-5]. The DMTJ critical switching current is decreased by the combined effect of the STT exerted from two antiparallel polarizers on the storage layer (SL). The retention is increased by doubling the SL/MgO interfaces contributing to the layer’s effective perpendicular anisotropy (PMA). Despite the efficiency advantage, the development of p-DMTJ presents several complexities. First, ensuring enough PMA for the top polarizer [6] and second, controlling the stray field compensation of both polarizers acting on the SL. A p-DMTJ structure having a switchable reference layer was recently proposed [7], showing through magnetic simulation similar performance as conventional p-DMTJ with static polarizers. In this work, we report experimental evidence of the working principle for this type of novel p-DMTJ. We fabricated and optimized an ASL-DMTJ structure having a switchable polarizer acting as a switching assistance layer (ASL). Fig.1.(a) and (b) show the two switching sequences, from AP to P and from P to AP states respectively. Both reversals show a transition through an intermediate resistance state (IRS). Differing from the working principles proposed in [7], the ASL is designed to switch its magnetic orientation during the write operation. This maximizes the STT efficiency independently of the switching direction, while also increasing the SL retention, since the assistance layer always aligns parallel to the SL in standby Fig.2(a)-(b). Validation of the switching sequences was possible by performing time-resolved resistance measurements Fig.2.(c)-(b).

![](https://assets.underline.io/uploads/markdown_image/1/image/0c7395b35a105c6f992d8d15bd884588.jpg)

Figure 1. (a) AP→P and (b) P→AP reversal sequence through an intermediated resistance state.

![](https://assets.underline.io/uploads/markdown_image/1/image/938d95a5c9fad68f070f40573955c5dd.jpg)

Figure 2. (a) RH of ASL-DMTJ (b) RV loops of ASL-DMTJ. (b) Real time AP→P switching sequence and (d) real time P to AP switching sequence both transiting through the IRS with (I) applied field, (II) at zero field.

Real time investigation of Double magnetic tunnel junction with switchable assistance layer for high efficiency STT-MRAM

Magnetoresistive random access memories (MRAM) based on spin-transfer torque are a scalable low-power memory solution being actively developed by industry, where the magnetization of a perpendicularly magnetized thin disk is reversed by a spin polarized current. During the switching process domain walls (DWs) can form [1-5] and understanding their dynamics in a disk geometry becomes of great interest. Here we show that DW surface tension always leads to oscillatory DW motion and instabilities that affect the switching dynamics. In a disk, a DW generally takes a circular form to minimize the sum of the surface and volume energies of the reversed domain, with the domain boundary perpendicular to the element boundary [1]. In a collective coordinate model the domain wall has two degrees of freedom, its position relative to the center of the disk q and the angle of the spins relative to the normal to the DW Φ. Here, Φ=0 is a Neel and Φ=90 deg. is a Bloch DW. We analyze the dynamics with micromagnetic and the collective coordinate model. Fig. 1a shows micromagnetic simulations of the evolution of the average perpendicular magnetization (mz) with fixed initial DW position q, such that mz&lt;0, and different initial angles Φ, with no field or current applied. Whereas both Neel and Bloch DWs states relax towards a reversed state (mz=-1), an intermediate initial angle DWs (Φ=45 deg) switches. Fig. 1b shows the (q,Φ) phase diagram indicating that the dynamics of domains close to the disk center is sensitive to small changes in Φ. Moreover, we show that the application of a spin-polarized current (I/Ic~0.2) mitigates the sensitivity of the final state to Φ. This opens a way to reduce the required power for a magnetization switching. The application of a short current pulse to nucleate a DW followed by a significant reduction of the pulse amplitude, well below the critical current, can still reliably switch the nanodisk (Fig. 2, blue curve); in this situation the power, which scales as I^2, can be drastically reduced.

![](https://assets.underline.io/uploads/markdown_image/1/image/ae7093247bc72273459ea2152e057f0d.jpg)

FIG. 1 a) Time evolution of mz for different Φ. b) (q,Φ) Relaxation phase diagram.

![](https://assets.underline.io/uploads/markdown_image/1/image/c3e1ee77b13cbb246c1c8906c9b8f14c.jpg)

FIG. 2 a) Time evolution of mz for two different currents after 5 ns. b) Spin-current amplitude as a function of the time.

Reduction of power consumption and micromagnetic instabilities associated with domain wall dynamics in perpendicular magnetic tunnel junction nanopillars

Reservoir computing (RC) is a computational framework where a fixed, non-linear reservoir maps the inputs into higher-dimensional space and the readout is trained with linear regression and classification [1]. Spintronic nanomagnetic devices are well suited for physical reservoir computing due to their interactive non-linear dynamics, recurrence characteristic, enduring lifetime, and low energy consumption [2-3]. A frustrated nanomagnet layer can also implement physical RC [4]. In this work, we demonstrate via micromagnetic simulation that a nanomagnet array excited by surface acoustic waves (SAW) can be used as a reservoir to classify sine and square waves with high accuracy. The simulated nanomagnet array has an input, 7 outputs (Fig. 1) and a 4 GHz SAW is applied to the input nanomagnet. The SAW amplitude is varied in such a way that its envelope forms a random sequence of sine and square waves of 100 MHz frequency (Fig. 2 (a)). The non-linear responses of the output nanomagnets due to this input are processed by reading the reservoir state every 1 ns while the period of the input is 10 ns. The envelopes of the output magnets’ magnetization are shown in Fig. 2 (b) which are used to train the output weights obtained by regression via Moore-Penrose pseudoinverse. For the classification, a random sequence of 100 square and sine wave samples are used. About 80 % of the total samples are trained, and the rest of the samples are used for testing. Various combination of the output nanomagnets exhibit 100 % training accuracy and 100 % testing accuracy which is indicative of the nanomagnet oscillator array being well suited for physical reservoir computing applications. Thus, this proposed strategy to implement reservoir computing could lead to energy efficient neuromorphic devices. Acknowledgment: M.F.F.C, W.A, D.B and J.A are supported in part by the National Science Foundation grant CCF-1815033.

![](https://assets.underline.io/uploads/markdown_image/1/image/ba0e1e285d3ea4a4474a2b29418b77a9.jpg)

Figure 1: A micro-magnetic snapshot of the input, the reservoir and output nanomagnets.

![](https://assets.underline.io/uploads/markdown_image/1/image/0bbde6989de89be932a0e0791dc0c7a5.jpg)

Figure 2: (a) SAW and input response (b) The envelopes of the responses vs. time (ns) of several output nanomagnets.

Surface Acoustic Wave Induced Nano-oscillator Based Reservoir Computing

In superparamagnetic tunnel junctions (SMTJ), the energy barriers that separate the parallel and the antiparallel magnetization configurations are sufficiently small that the thermal noise causes stochastic magnetization fluctuations between the two states. Through magnetoresistance effects, those stochastic fluctuations give rise to telegraphic noise signals with two voltage levels (see Fig.1) [1, 2]. The relative average time spent in each of the two configurations can be controlled by an applied dc current via the spin-transfer torque. Due to their inherent stochastic nature, SMTJs are promising candidates for truly random number generator applications [3, 4], brain-inspired and probabilistic computing [5-10]. However, an external bias magnetic field was always required to fully operate the SMTJ’s with current, which represents a drawback to their integration with the required microelectronic circuits. In this work, we report for the first time, SMTJs that are fully controlled by DC current at zero applied magnetic field using perpendicular magnetic tunnel junctions (pMTJ). Stable pMTJs are used in densely magnetic memories [11], nevertheless their thermal stability can be strongly reduced by tuning their free layer thickness, leading to a superparamagnetic state. Here, we use pMTJ’s with a free layer thickness of t=1.6 nm, and with diameters ranging from 150 nm to 80 nm. By applying a DC current in one direction (sign), without any external bias field, we observe stochastic fluctuations in the resistance for dc currents between -0.3 mA and -0.27 mA. As shown in Fig. 2, the average probability in being in high and low resistance state is well controlled by the applied current. In order to obtain good statistics on the extracted probability, we record to stochastic transitions. We extracted the dwell time distribution, which follows an exponential distribution with a mean dwell time that is in agreement with the modified Néel-Brown model. The authors acknowledge partial funding from ERC MAGICAL No 669204, and the Nanoscience Foundation (Grenoble, France).

![](https://assets.underline.io/uploads/markdown_image/1/image/032d011551d8698e3dc1b74244af9c81.jpg)

Figure 1 : Experimental voltage time trace of the SMTJ.

![](https://assets.underline.io/uploads/markdown_image/1/image/f8c0fe8daabe77b0c738c5d388eadce3.jpg)

Figure 2 : The probability of occupancy of the high resistance state versus current.

Current controlled perpendicular superparamagnetic tunnel junctions operating at zero applied magnetic field

Spin-transfer torque (STT) based magnetoresistive random-access memory (MRAM) is of great current interest for embedded applications, due to its high speed, high endurance, and long retention time. However, state-of-the-art STT MRAM requires a large switching current that leads to reliability issues, high write energy, and limits the density scaling. In this talk, we will show that a conventional current-controlled MTJ can be changed to a voltage-controlled device if we replace the thin oxide barrier with a resonant tunneling barrier. Such a simple modification will combine two established quantum phenomena: interlayer exchange coupling and resonant tunneling within the scaled magnetic structure to enable a resonant-exchange controlled (REC) magnetization switching [1]. REC-driven magnetization switching can substantially lower the write energy, decouple the energy and speed of operations, eliminate the reliability issues, and enable denser MRAM technologies. A voltage-controlled MTJ has a high baseline resistance, which may cause compatibility issues with standard CMOS. We will discuss a magnetic gain cell structure that consists of a REC MTJ and two transistors for a large separation in the output current for the two memory states while retaining the low power advantages of a voltage-controlled write mechanism [2]. We analyze the REC MTJ using quantum-transport simulations on a CoFeB/MgO/Ru/MgO/CoFeB structure and combine the results with an empirical SPICE model to analyze the magnetic gain cell using 45 nm CMOS models in HSPICE. Our simulation results suggest approximately 103 times change in the read current in the gain cell between the two magnetic states. The expected energy and delay are 29.5 fJ/bit and 1.6 ns respectively for a write operation and 7.6 ~ 39 fJ/bit and 0.6 ~ 1 ns respectively for a read operation, thus promising for low energy, fast, and high-density REC MRAM technology.

![](https://assets.underline.io/uploads/markdown_image/1/image/8f55c819fa0feacf6498620a60124061.jpg)

A magnetic gain-cell structure for efficient voltage-controlled MRAM

Voltage control of magnetism provides a promising path toward the development of low-power spintronic devices. Magneto-ionics exploiting voltage-driven ion migration as a control mechanism has attracted interest because it can generate large magnetoelectric effects at low voltage. Here, we demonstrate the use of all-solid-state lithium-ion battery technology for reversible voltage-controlled switching between perpendicular and in-plane magnetization states in a Co/Pt bilayer. The magneto-ionic battery structure consists of a 2 nm Ta/5 nm Pt/20 nm LiCoO2/70 nm LiPON/3 nm Co/5 nm Pt crossbar junction (Fig. 1a,b). Here, the LiPON film functions as a solid-state electrolyte and the LiCoO2 acts as the Li+ storage layer. Using magneto-optical Kerr effect (MOKE) microscopy, we show that the magnetization of the Co film switches between in-plane and perpendicular directions when voltages of +2.5 V and –2 V are applied (Fig. 1c,d). The magneto-ionic switching effect is reliably reproduced over multiple cycles (Fig. 2a,b) and switching takes less than 0.5 s at room temperature (Fig. 2c). Our structure can be cycled for &gt;500 times without degradation. The magnetoelectric coupling efficiency is estimated to be 7,700 fJ/Vm at room temperature, which is the strongest magnetoelectric effect reported to date. We attribute the effect to changes in the hybridization of electron orbitals at the Co/Pt interface upon voltage-controlled insertion and deinsertion of Li+ ions. Our work sets a new benchmark for lithium-based magneto-ionics and demonstrates the relevance of battery design principles for the development of low-power spintronic devices.

![](https://assets.underline.io/uploads/markdown_image/1/image/0e0e0a222892a8e22e21ad864ff48d48.jpg)

Fig. 1 (a) Schematic of the magneto-ionic battery structure. (b) Optical microscopy image of a single crossbar junction. (c) Polar and (d) longitudinal MOKE hystersis curves recorded at –2.0 and +2.5 V.

![](https://assets.underline.io/uploads/markdown_image/1/image/7f80d4c4edc9f54a6a38f9541a3f553c.jpg)

Fig. 2 Reversible voltage-controlled switching of (a) the perpendicular magnetization and (b) the in-plane magnetization in zero magnetic field. (c) Time-resolved modulation of the longitudinal MOKE signal at room temperature recorded while toggling the voltage between –2.0 V and +2.5V in 0.5 s intervals.

Lithium-Ion Battery Technology for Voltage Control of Perpendicular Magnetization

The application of gate voltages in ferromagnet/Oxide/Heavy metal thin film multilayer has been identified as one possible candidate to manipulate their anisotropy at will [3], [1], [4]. Unfortunately, this method has proven to be unreliable in terms of the reversibility: e.g, the application of a gate voltage onMgO/CoFeB/Ta [3] causes an irreversible change of the magnetic anisotropy axis from in plane to out-of-plane, while HfO2/CoFeB/Ta stacks display a more complicated behavior, with different regimes characterized by different reversibility behaviors [2]. The microscopic origin of these phenomena remains unclear and is the object of our inquiry. In this work we performed ab-initio simulations on 6 setups comprising Fe/O, HfO2/Fe, MgO/Fe interfaces each one with frontal and interstitial oxygen atomic configurations (fig.1). The pure Fe/O interface prefers interstitial oxygen configurations while the addition of oxides seems to switch this trend around. In particular, HfO2/Fe displays a strong preference for the frontal oxygen arrangement, leading us to identify a possible hypothesis for the phenomenon of the multiple magneto-ionic regimes displayed. In it’s as grown form, HfO2/CoFeB is characterized by an underoxidized ferromagnet interface [2]. The application of a gate voltage on HfO2/CoFeB pushes some of the oxygen species in proximity of the interface where they occupy the stable frontal site. Further application of a gate voltage pushes other oxygen atoms toward the still free and more unstable interstitial sites. The occupation of these interstitial sites has the effect of destroying perpendicular magnetic anisotropy as seen both in experiments and simulations [5] [1]. Once these more mobile interstitial sites are occupied, magnetic anisotropy can be switched reversibly by inverting the gate voltage and adjusting the interstitial site occupation (fig.2)

![](https://assets.underline.io/uploads/markdown_image/1/image/40c593503d5a4f3ee9c27db4ad2dc04d.jpg)

Comparison of the different interfacial configurations in the differentsamples. The bar plots represent the ground state energies and highlight themore stable configuration

![](https://assets.underline.io/uploads/markdown_image/1/image/f5dc5807d0f4552079d7a39375420a9e.jpg)

Hypothesis for the mechanism governing magneto-ionic regimes inHfO2/FM stacks. The text highlights the magnetic properties of the interface under different oxidation conditions.

Reversible anisotropy manipulation via gate voltages in ferromagnet/oxide multilayers

Magnetoionics (MI) is a much-in-demand feature for spintronics devices due to its great potential for improving energy efficiency in memory devices. The main feature of MI is to allow for the nonvolatile modulation of magnetic properties [1–5], making MI reversibility a fundamental quality for high device performance. Various studies have addressed issues impacting reversibility such as the presence of different types of ions, oxides and the degree of oxidation at the oxide/ferromagnetic interface [3, 5–8], while the influence of annealing temperature on reversibility has been largely overlooked. Annealing is commonly used to induce crystallization in CoFeB layers leading to the enhancement of perpendicular magnetic anisotropy (PMA), interfacial Dzyaloshinskii-Moriya interaction (iDMI), and tunnel magnetoresistance [9–11], therefore its impact on MI is of great interest. In this study, we show that W/CoFeB/HfO2 thin films annealed at 350°C and 390°C show a significant but irreversible dependence of PMA and DMI on gate voltage. However, an annealing program using a slow ramp that minimizes the exposure to the highest temperatures allows to induce PMA and iDMI while also showing a MI reversibility of 71% in remanence with respect to PMA. This dependence of MI reversibility on annealing profiles and temperatures could be related to the different degrees of crystallinity obtained under different annealing conditions. Such structural differences, such as size and distribution of crystallites, can greatly impact the ion mobility inside the stack and be responsible for observed modulation of the MI reversibility. Our results therefore show that annealing parameters need to be optimized to obtain enhanced magnetic properties without compromising MI performance.

![](https://assets.underline.io/uploads/markdown_image/1/image/54d50bab8c546ddbc8fd33ecfea28690.jpg)

![](https://assets.underline.io/uploads/markdown_image/1/image/bf6e6dbd3a0f9277642eaa6aab5dd034.jpg)

Effect of Annealing on Magnetoionics in W/CoFeB/HfO2

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Complex Hopfield Networks Implemented Using Coupled Spin-Torque Oscillator Arrays

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