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The rate of thermally activated transitions can generally be described by the Arrhenius law, as &lt;i&gt;k=f&lt;/i&gt;&lt;sub&gt;&lt;i&gt;0&lt;/i&gt;&lt;/sub&gt;&lt;i&gt; &lt;/i&gt;exp(&lt;i&gt;-ΔE/k&lt;/i&gt;&lt;sub&gt;&lt;i&gt;B&lt;/i&gt;&lt;/sub&gt;&lt;i&gt;T&lt;/i&gt;). In the previous expression, &lt;i&gt;Δ&lt;/i&gt;E is the internal energy barrier, and &lt;i&gt;f&lt;/i&gt;&lt;sub&gt;&lt;i&gt;0&lt;/i&gt;&lt;/sub&gt; is a prefactor that encompasses both dynamical and entropic contributions. In this work, we show how the activation entropy can play a crucial role in thermally activated magnetic transitions. To that end, we study two types of transitions involving solitons either as the stable state or the transition state, namely, skyrmion annihilation, and domain-wall mediated magnetization reversal in nanodisks.&lt;br/&gt;&lt;br/&gt;The aforementioned transitions pertain to the class of rare events, i.e., they are unlikely to arise in the course of a Langevin simulation, because the mean waiting time between events is much larger than the integration timestep. In this case, one possible approach is to use a form of reaction rate theory to calculate the energy barrier and the Arrhenius prefactor. To this end, we apply Langer’s theory [1], which is an extension of the Kramers method to many dimensions. Alternatively, one may use a path sampling method such as forward flux sampling [2] in order to sample the transition path ensemble from the system’s dynamics and extract a transition rate. In this work, we employ both methods in conjunction to study the thermal stability of magnetic skyrmions and nanodisks, for which the two methods show a good agreement.&lt;br/&gt;&lt;br/&gt;Magnetic skyrmions are particle-like, two-dimensional, non-collinear solitonic spin textures with a nontrivial topology. In ultrathin films and multilayers, small skyrmions are stabilized by an interfacial form of the Dzyaloshinskii-Moriya interaction (DMI). Skyrmion-based designs currently appear very promising for novel spintronics devices, e.g., for racetrack memories and logic gates, reservoir computing, or probabilistic computing [3]. Isolated skyrmions exist as metastable excitations of the collinear magnetic ground state and can be longed-lived. For applications, a stability of up to 10 years at room temperature is a technological requirement, and the ability to accurately predict that stability is therefore crucial. We find that skyrmions can be stabilized not only by an internal energy barrier, but also by a large activation entropy. This stems from the fact that the metastable skyrmion state possesses a large entropy compared to the transition state, because skyrmions exhibit stable internal modes of deformation [4]. When applying a destabilizing magnetic field perpendicular to the skyrmion core, the skyrmion lifetime decreases quickly with the field amplitude. This results from a shrinking of the skyrmion, which simultaneously reduces the the energy barrier, as well as the activation entropy through the suppression of the stable internal modes (Fig. 1) [5].&lt;br/&gt;&lt;br/&gt;Next, we compute the mean waiting times between thermally activated domain-wall mediated magnetization reversals in a perpendicularly magnetized nanodisk. We use parameters that resemble that of a free CoFeB layer, as typically used in magnetoresistive random access memories [6]. By varying the perpendicular anisotropy and the interfacial DMI, we find that the Arrhenius prefactor can take extreme, seemingly non-physical values up to 10&lt;sup&gt;21&lt;/sup&gt; Hz, which is orders of magnitude beyond the typically assumed value of 10&lt;sup&gt;9&lt;/sup&gt; Hz, and vary drastically as a function of material parameters. We show that the prefactor behaves like an exponential of the energy barrier, which stems from a linear variation of the activation entropy with the energy barrier (Fig. 2) [7]. This phenomenon, known as the Meyer-Neldel rule, or entropy-enthalpy compensation, has been reported across diverse fields of the natural sciences for over a century—in semiconductors, chemical reactions, biological death rates, etc [8]. In magnetism, it was only recently observed in the decay of the skyrmion lattice [9]. This result implies that the transition state, which, in the case of the magnetization reversal, is a domain wall found in the center of the disk, tend to possess a very large entropy compared to the collinear ground state. This is verified by the existence of gapless, low energy modes propagating along domain walls [10].&lt;br/&gt;&lt;br/&gt;These findings suggest that the Arrhenius prefactor, which is typically taken as an attempt frequency within the characteristic timescale of a magnetic system&#39;s dynamics, may in some cases contain a very large entropic contribution which should not be neglected. This seems to be particularly true in magnetic transitions involving solitons.

**References:**

[1] J. S. Langer, Annals of Physics, Vol. 54, p.258 (1969) [2] R. J. Allen, P. B. Warren, and P. R. ten Wolde, Phys. Rev. Lett., Vol. 94, p.018104 (2005) [3] A. Fert et al., Nat. Nanotechnol., Vol. 3, p.152 (2013) ; Prychynenko et al., Phys. Rev. Appl., Vol. 9, p.014034 (2018) ; D. Pinna et al, Phys. Rev. Appl., Vol. 9, p.064018 (2018) [4] L. Desplat, D. Suess, J.-V. Kim et al., Phys. Rev. B, Vol. 98, p.134407 (2018) [5] L. Desplat, C. Vogler, D. Suess et al., Phys. Rev. B, Vol. 101, p.060403(R) (2020) [6] J. Sampaio, A. Khvalkovskiy, M. Kuteifan et al., Appl. Phys. Lett., Vol. 108, p.112403 (2016) [7] L. Desplat and J-.V. Kim, Phys. Rev. Lett., Vol 125, p.107201 (2020) ; L. Desplat and J-.V. Kim, Phys. Rev. Appl., Vol. 14, p.064064 (2020) [8] A. Yelon, B. Movaghar, and R. Crandall, Rep. Prog. Phys., Vol. 69, p.1145 (2006) [9] J. Wild, T. N. Meier, S. Pollath et al., Sci. Adv., Vol. 3, p.e1701704 (2017) [10] F. Garcia-Sanchez, P. Borys, R. Soucaille, et al., Phys. Rev. Lett., Vol. 114, p.247206 (2015)

**Images:**

![](https://s3.eu-west-1.amazonaws.com/underline.prod/uploads/markdown_image/1/image/3d6013214984a1f3ea7698c4d79f4b7a.jpg)
Arrhenius prefactor for skyrmion collapse as a function of the perpendicular external magnetic field in units of the exchange coupling constant, Jex. The prefactor is computed through Langer&#39;s theory, forward flux sampling (FFS), and direct Langevin simulations.

![](https://s3.eu-west-1.amazonaws.com/underline.prod/uploads/markdown_image/1/image/1c239dc0374a9439b358445c11e10d48.jpg)
Activation entropy for domain-wall mediated magnetization reversal in a nanodisk at T = 300 K as a function of the activation energy normalized by the exchange constant, &lt;i&gt;A&lt;/i&gt;, and the disk thickness &lt;i&gt;d&lt;/i&gt;, for variations of the DMI, D, and of the anisotropy, K. The inset shows the corresponding variation of the Arrhenius prefactor, &lt;i&gt;f&lt;/i&gt;&lt;i&gt;0&lt;/i&gt;.

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 Entropic effects and solitons in thermally activated magnetic transitions INVITED

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The rate of thermally activated transitions can generally be described by the Arrhenius law, as k=f0 exp(-ΔE/kBT). In the previous expression, ΔE is the internal energy barrier, and f0 is a prefactor that encompasses both dynamical and entropic contributions. In this work, we show how the activation entropy can play a crucial role in thermally activated magnetic transitions. To that end, we study two types of transitions involving solitons either as the stable state or the transition state, namely, skyrmion annihilation, and domain-wall mediated magnetization reversal in nanodisks. The aforementioned transitions pertain to the class of rare events, i.e., they are unlikely to arise in the course of a Langevin simulation, because the mean waiting time between events is much larger than the integration timestep. In this case, one possible approach is to use a form of reaction rate theory to calculate the energy barrier and the Arrhenius prefactor. To this end, we apply Langer’s theory [1], which is an extension of the Kramers method to many dimensions. Alternatively, one may use a path sampling method such as forward flux sampling [2] in order to sample the transition path ensemble from the system’s dynamics and extract a transition rate. In this work, we employ both methods in conjunction to study the thermal stability of magnetic skyrmions and nanodisks, for which the two methods show a good agreement. Magnetic skyrmions are particle-like, two-dimensional, non-collinear solitonic spin textures with a nontrivial topology. In ultrathin films and multilayers, small skyrmions are stabilized by an interfacial form of the Dzyaloshinskii-Moriya interaction (DMI). Skyrmion-based designs currently appear very promising for novel spintronics devices, e.g., for racetrack memories and logic gates, reservoir computing, or probabilistic computing [3]. Isolated skyrmions exist as metastable excitations of the collinear magnetic ground state and can be longed-lived. For applications, a stability of up to 10 years at room temperature is a technological requirement, and the ability to accurately predict that stability is therefore crucial. We find that skyrmions can be stabilized not only by an internal energy barrier, but also by a large activation entropy. This stems from the fact that the metastable skyrmion state possesses a large entropy compared to the transition state, because skyrmions exhibit stable internal modes of deformation [4]. When applying a destabilizing magnetic field perpendicular to the skyrmion core, the skyrmion lifetime decreases quickly with the field amplitude. This results from a shrinking of the skyrmion, which simultaneously reduces the the energy barrier, as well as the activation entropy through the suppression of the stable internal modes (Fig. 1) [5]. Next, we compute the mean waiting times between thermally activated domain-wall mediated magnetization reversals in a perpendicularly magnetized nanodisk. We use parameters that resemble that of a free CoFeB layer, as typically used in magnetoresistive random access memories [6]. By varying the perpendicular anisotropy and the interfacial DMI, we find that the Arrhenius prefactor can take extreme, seemingly non-physical values up to 1021 Hz, which is orders of magnitude beyond the typically assumed value of 109 Hz, and vary drastically as a function of material parameters. We show that the prefactor behaves like an exponential of the energy barrier, which stems from a linear variation of the activation entropy with the energy barrier (Fig. 2) [7]. This phenomenon, known as the Meyer-Neldel rule, or entropy-enthalpy compensation, has been reported across diverse fields of the natural sciences for over a century—in semiconductors, chemical reactions, biological death rates, etc [8]. In magnetism, it was only recently observed in the decay of the skyrmion lattice [9]. This result implies that the transition state, which, in the case of the magnetization reversal, is a domain wall found in the center of the disk, tend to possess a very large entropy compared to the collinear ground state. This is verified by the existence of gapless, low energy modes propagating along domain walls [10]. These findings suggest that the Arrhenius prefactor, which is typically taken as an attempt frequency within the characteristic timescale of a magnetic system's dynamics, may in some cases contain a very large entropic contribution which should not be neglected. This seems to be particularly true in magnetic transitions involving solitons.

**References:**

[1] J. S. Langer, Annals of Physics, Vol. 54, p.258 (1969) [2] R. J. Allen, P. B. Warren, and P. R. ten Wolde, Phys. Rev. Lett., Vol. 94, p.018104 (2005) [3] A. Fert et al., Nat. Nanotechnol., Vol. 3, p.152 (2013) ; Prychynenko et al., Phys. Rev. Appl., Vol. 9, p.014034 (2018) ; D. Pinna et al, Phys. Rev. Appl., Vol. 9, p.064018 (2018) [4] L. Desplat, D. Suess, J.-V. Kim et al., Phys. Rev. B, Vol. 98, p.134407 (2018) [5] L. Desplat, C. Vogler, D. Suess et al., Phys. Rev. B, Vol. 101, p.060403(R) (2020) [6] J. Sampaio, A. Khvalkovskiy, M. Kuteifan et al., Appl. Phys. Lett., Vol. 108, p.112403 (2016) [7] L. Desplat and J-.V. Kim, Phys. Rev. Lett., Vol 125, p.107201 (2020) ; L. Desplat and J-.V. Kim, Phys. Rev. Appl., Vol. 14, p.064064 (2020) [8] A. Yelon, B. Movaghar, and R. Crandall, Rep. Prog. Phys., Vol. 69, p.1145 (2006) [9] J. Wild, T. N. Meier, S. Pollath et al., Sci. Adv., Vol. 3, p.e1701704 (2017) [10] F. Garcia-Sanchez, P. Borys, R. Soucaille, et al., Phys. Rev. Lett., Vol. 114, p.247206 (2015)

**Images:**

![](https://s3.eu-west-1.amazonaws.com/underline.prod/uploads/markdown_image/1/image/3d6013214984a1f3ea7698c4d79f4b7a.jpg)
Arrhenius prefactor for skyrmion collapse as a function of the perpendicular external magnetic field in units of the exchange coupling constant, Jex. The prefactor is computed through Langer's theory, forward flux sampling (FFS), and direct Langevin simulations.

![](https://s3.eu-west-1.amazonaws.com/underline.prod/uploads/markdown_image/1/image/1c239dc0374a9439b358445c11e10d48.jpg)
Activation entropy for domain-wall mediated magnetization reversal in a nanodisk at T = 300 K as a function of the activation energy normalized by the exchange constant, A, and the disk thickness d, for variations of the DMI, D, and of the anisotropy, K. The inset shows the corresponding variation of the Arrhenius prefactor, f0.

technical paper

Reservoir computing has been gaining traction as a bio-inspired, machine-learning approach for solving computationally hard problems, such as speech recognition and prediction of chaotic time series, that are becoming fundamental to modern technology [1]. By making use of a complex, fixed reservoir with a single output layer, the approach minimises expensive training steps. Magnetic systems are well positioned to fulfill the requirements of the reservoir, providing non-linearity, fading memory, and reproducible responses. In particular, magnetic systems subject to a temporal input sequence can perform the entire role of the reservoir, replacing complex networks of transistors whilst only requiring a single input and output [2]. This offers the potential for compact devices, with reduced energy costs for computation. Here, we propose strain-mediated, voltage controlled superparamagnetic ensembles as an ideal candidate for an ultra-low-energy reservoir. Single superparamagnetic nano-dots with uniaxial anisotropy exhibit stochastic behavior, switching between states with a characteristic timescale set by their energy barriers. Micromagnetic simulations (Fig. 1a) demonstrate this telegraphing behavior for CoFeB nano-dots, where the auto-correlation time has been verified to be below 5 ns and the strain-induced anisotropy can compete with the intrinsic anisotropy [3-6]. Borders et al. [7] have shown how tuning such telegraph noise in magnetic tunnel junctions can be used for integer factorization. Here, however, we make use of the collective behavior of extended ensembles of nano-dots such that the average magnetisation becomes predictable; thus, showing the reproducible response required for reservoir computing. Input is provided by addition of a strain-mediated anisotropy (Fig. 1b)—a technique that is predicted to achieve ultra-low-power consumption [8]. This acts to rotate the intrinsic anisotropy axis, biasing dwell time in one state over the other [9]. Whilst the internal system dynamics are driven by thermal noise, the average magnetisation obeys simple rules that, when subject to a temporally varying input, produce a complex non-linear response with fading memory (Fig. 1c). Using an analytical model of the response of the superparamagnetic ensemble [10], we simulate the physical reservoir. By inputting a temporal sequence and training a single layer of weights from the output, we can perform standard machine learning benchmarks with competitive performance over a range of input timescales. Fig. 2 presents the performance for spoken digit recognition on the TI-46 dataset and chaotic time series prediction on the NARMA10 task. In both cases the performance approaches that of competing reservoirs [1,2], despite the relative simplicity of the system. The intrinsic timescale of the system (τ) is controlled by the ratio of energy barrier (anisotropy, KV) to the thermal energy (KBT at 300 K). Fig. 2a demonstrates that by tuning the input rate and strength (relative to the intrinsic timescale and anisotropy respectively), optimal performance of 95 % recognition of the spoken digits 0-9 with five female speakers can be achieved. This is realised for τ ~ 50 ns (KV/KBT = 20) and τ ~ 1 ms (KV/KBT = 50). For the NARMA10 task (Fig. 2b), the highest performance is achieved across this entire timescale range by tuning the strength of a feedback term (i.e. the output is fed back into the input stream with a given weight). Robust performance on timescales ranging from hundreds of nanoseconds up to seconds would allow a physical realisation of such a reservoir to be tuned to provide computation in real time for a wide range of possible physical inputs: from decision-making in driverless cars (fast) to speech recognition (slow). The simplicity of the system and the ease of manufacture, coupled with the low energy consumption expected for such a, thermally driven, device makes it an ideal candidate for edge computing where high performance is needed at very low latency and power.

**References:**

[1] L. Appeltant et al., Nat Commun. 2, 468 (2011). [2] J. Torrejon et al., Nature. 547, 428–431 (2017). [3] A. Vansteenkiste et al., AIP Advances. 107133 (2014). [4] J. Leliaert et al., AIP Advances. 7, 125010 (2017). [5] S. Zhang et al., Scientific Reports. 4, 3727 (2014). [6] C. Safranski et al., arXiv:2010.14393[cond-mat] (2020). [7] W. A. Borders et al., Nature. 573, 390–393 (2019). [8] K. Roy, S. Bandyopadhyay and J. Atulasimha, Applied Physics Letters. 99 (2011). [9] Z. Guo et al., Appl. Phys. Lett. 112, 052904 (2018). [10] O. Hovorka et al., Appl. Phys. Lett. 97, 062504 (2010).

**Images:**

![](https://s3.eu-west-1.amazonaws.com/underline.prod/uploads/markdown_image/1/image/2ab92e894162fe72ba365c3a5186c89a.jpg)
Fig. 1: a. Telegraphing behaviour of a single superparamagnetic nano-dot. Micromagnetic simulation for a 40 nm diameter, 4 nm thick CoFeB cylinder with 8.2 KJ/m-3 uniaxial anisotropy at 300 K. b. Rotation of the intrinsic anisotropy axis in a nano-dot via strain anisotropy. The rotation biases the dwell time in one magnetic state (left/right pointing) versus the other. c. Analytical response of an ensemble of superparamagnetic nano-dots to a global input via strain. A temporal input sequence produces the non-linear response with fading memory required for reservoir computing.

![](https://s3.eu-west-1.amazonaws.com/underline.prod/uploads/markdown_image/1/image/7c8ac57f39d07c9f25ded428f97ed89f.png)
Fig 2: a. Performance on a spoken-digit recognition test using the TI-46 dataset. Performance is tuned by the input rate (relative to the intrinsic timescale) and strength. Optimal performance (95 % recognition accuracy) is achieved for intrinsic timescales of 50 ns (KV/KBT = 20, left) and 1 ms (KV/KBT = 50, right) and approaches that of competing reservoirs [2]. b. Performance on a chaotic time series prediction task (NARMA10). Performance is maintained across the entire timescale range by tuning a feedback parameter.

 Voltage-Controlled, Thermally Driven Superparamagnetic Ensembles for Tuneable Timescale Reservoir Computing

The spintronic devices such as the domain wall and skyrmion based devices are being explored for their applications in neuromorphic computing [1-3], due to their non-volatile behaviour, stochastic switching and energy efficiency. The electric field control of these devices has attracted extensive attention and has further gained popularity both in memory and logic applications as it provides an efficient way to improve the data storage density [4-8]. Neuromorphic computing has been shown to be a superior alternative in terms of speed and energy, when compared to training on a conventional computing unit. A magnetic tunnel junction (MTJ), which constitutes the building block of a magnetic random-access memory (MRAM), is a competitive candidate for the next generation memory devices because it is non-volatile and exhibits high endurance, low power consumption, high operation speed and integration capability [8-9]. The present work discusses the proposal of a spintronic neuromorphic system with spin orbit torque driven domain wall motion-based neuron and synapse. The domain wall motion in the neuron is controlled by the electric field at the gate generated by voltage signals from pre-neurons as shown in Fig. 1(a). The application of voltage as input and output variable helps in reducing power consumption, as pinning site can be turned ON/OFF only when required. Further it provides better fanout as post-neuron output from first stage can act as infinite number of pre-neurons to next stage, which is going to play an important role in realization of system level neuromorphic architectures. For the implementation of the synaptic weight, we propose 3- terminal MTJ with stochastic domain wall motion in the free layer. The edge roughness results in intrinsic pinning of domain wall and this adds to the stochastic behaviour of domain wall motion in presence of spin transfer torque and/or spin orbit torque [10]. We have incorporated these intrinsic pinning effects by creating triangular notches on the sides of free layer as shown in Fig. 1(b). The complete neuromorphic circuit containing neurons, synapses and control circuitry implementation of spike time dependent plasticity is shown in Fig. 1(a). The sensing unit consists of a voltage comparator which provides high positive or zero voltage feedback pulse to the weight-updating unit. The weight updating unit takes this signal and the pre-neuron signal to decide the sign of the synaptic weight update pulse based upon spike timing correlation between the pre- and post-neuron. For modelling the micromagnetics and spin transport in both synapse and neuron, we developed a micromagnetic Non-Equilibrium Green’s Function (MuMag-NEGF) coupled model. In Fig. 2(a), we show that in the presence of the gate voltage the uniaxial anisotropy for a 20 nm-wide region is varied slightly which results in the pinning of domain wall at 40 nm from the centre. In the absence of gate voltage uniaxial anisotropy is uniform and the domain wall moves more than 100 nm right in 10 ns, where it is sensed by the MTJ as post neuron spike. Also, the reduction of domain wall velocity is shown in Fig. 2(b). The thermal effects result in the stochastic domain wall motion but stochasticity is tuned by the external current in the form of spin orbit torque. Fig. 2 (c) shows the stochastic magnetization switching in MTJ synapse and stochasticity is tuned in either direction with application of positive and negative current pulses. In the presence of more positive pulses, the MTJ gradually moves into low resistance state, while in the presence of more negative pulses it moves into high resistance state, as shown in Fig. 2(d). When a current pulse is applied, the probability of domain wall moving increases. The dynamic pattern recognition capabilities of the circuit are tested on 9x9 cross-bar architecture, consisting of 9 pre-neurons, 9 post-neurons and 81 synapses. In summary, here we have successfully designed and demonstrated the on-chip stochastic dynamic learning by adopting the spike time dependent plasticity as the learning algorithm. At the end of training phase, we demonstrated that all pattern synapses stochastically evolve into low resistance state (high weight) while the remaining background synapses are in high resistance state (low weight).

**References:**

[1]. Deming Zhang, Weisheng Zhao et. al, “All spin neural networks based on compound spintronic synapses and neurons,” IEEE Advances in Biomedical Circuits and Systems, vol. 10, no.4, August-2016. [2]. Otitoaleke Akinola, Jean Anne C Incorvia et.al, “Three terminal magnetic tunnel junction circuits showing spike timing-dependent plasticity,” J. Phys. D: Appl. Phys. 52 (2019) 49LT01 (11pp). [3]. Yangqi Huang, Weisheng Zhao et.al, “Magnetic-skyrmion based synaptic device,” IOP Nanotechnology, 28 08LT02, 2017 [4]. Jiang Nan, Weisheng Zhao et.al, “Efficient Magnetic Domain Nucleation and Domain Wall Motion with Voltage Control Magnetic Anisotropy Effect and Antiferromagnetic/Ferromagnetic Coupling,” IEEE TRANSACTIONS ON MAGNETICS, Vol. 55, No. 7, JULY 2019. [5]. Jialin Cai et.al, “Voltage-Controlled Spintronic Stochastic Neuron Based on a Magnetic Tunnel Junction,” Phys. Rev. Applied 11, 034015, 6 March 2019.[6]. M. Bauer, R. Alexis, G. Atwood, B. Baltar, A. Fazio, K. Frary, M. Hensel, M. Ishac, J. Javanifard, M. Landgraf, D. Leak, K. Loe, D. Mills, P. Ruby, R. Rozman, S. Sweha, S. Talreja, and K. Wojciechowski, in IEEE International Solid-State Circuits Conference Digest of Technical Papers (1995), p. 132. [7]. T. S. Jung, Y. J. Choi, K. D. Suh, B. H. Suh, J. K. Kim, Y. H. Lim, Y. N. Koh, J. W. Park, K. J. Lee, J. H. Park, K. T. Park, J. R. Kim, J. H. Yi, and H. K. Lim, IEEE J. Solid-State Circuits 31, 1575 (1996). [8]. X. H. Lou, Z. Gao, D. V. Dimitrov, and M. X. Tang, Appl. Phys. Lett. 93, 242502 (2008). [9]. N. Locatelli, V. Cros, and J. Grollier, Nat. Mater. 13, 11 (2014). [10]. F. N. Tan et.al, “High Velocity Domain wall propagation using voltage control magnetic anisotropy,” Scientific Reports, 2019) 9:7369.

**Images:**

![](https://s3.eu-west-1.amazonaws.com/underline.prod/uploads/markdown_image/1/image/11865266d897f1c54b8d59f2476fef1b.jpg)
Fig. 1: (a) Neuromorphic circuit implementation of spike time dependent plasticity-based pattern recognition. 
The circuit consists of voltage-controlled magnetic anisotropy post neuron and stochastic 3T-MTJ synapse with domain wall driven by SOT. (b) 3T-MTJ stochastic synapse schematic with small notches. (c) Cross-bar architecture implementation of the circuit.

![](https://s3.eu-west-1.amazonaws.com/underline.prod/uploads/markdown_image/1/image/568d7875203ba7028c8a7d9c8698bfa0.jpg)
Fig. 2: (a) Domain wall position in neuron for pinning ON and pinning OFF controlled by gate voltage. (b) Domain wall velocity for the pinning and non-pinning case. (c) Magnetization in the free layer MTJ synapse showing stochastic behavior with a positive current pulse. (d) MuMAG – NEGF coupled computed resistance evolution of the synapse.

 Domain Wall Motion based Neuromorphic Computing with Voltage Controlled Spin Neuron and Stochastic Magnetic Tunnel Junction Synapse

Physical reservoir computing using spin-torque oscillator (STO) has been attracting much attention after the demonstration of highly accurate human voice recognition [1]. To enhance the computational ability of STOs, there are two approaches: an array of STOs, and delayed feedback system. In the STO array, theoretical study was performed on 10-1000 STOs with interaction to obtain highly nonlinear dynamics, resulting in enhancement of computational ability that is competitive to quantum reservoir [4]. A similar highly nonlinear dynamics can be generated by using the feedback system, where outputted power from STO was reinjected in the form of electrical current or magnetic field to induce additional torque. By tuning the additional torque, complex magnetization dynamics such as amplitude modulation [5] and chaos [6,7] can be induced. These nonlinear dynamics are expected to enhance the computational ability even without interactions between STOs. Recent study showed that tunable nonlinear dynamics and delayed response by the feedback effect are adaptable for specific temporal pattern recognition task [7]. However, quantitative comparison of computational ability between the array system and the feedback system has not been investigated. In this work, we quantitatively evaluate the computational ability of STO with delayed-feedback circuit by performing a general task. The computational ability was evaluated by performing second order nonlinear autoregressive moving average (NARMA2) task [3,4]. The NARMA2 task aims to reproduce target outputs, defined by nonlinear transformation of the random inputs, from the amplitude of the STO. Figure 1 is a schematic illustration of vortex-type STO [2] with a delayed feedback circuit. To excite an auto-oscillation of the vortex core, perpendicular magnetic field of about 0.5 T and bias voltage of 350 mV were applied. In addition, we superposed random-pulse voltage with the width of 25 ns on the bias voltage. The random pulse was based on the uniform random number in the range of [-1,1] with the magnitude of 75 mV; therefore, the total input voltage varied from 275 to 425 mV. The outputted signal of the STO was amplified and converted to the RF magnetic field, which was applied to the STO with time delay of about 30 ns. The strength of the feedback signal was controlled by a tunable attenuator. Figure 2(a) shows power spectral density of the STO with delayed feedback as a function of attenuation. In the large attenuation region, the power spectra show a broad peak. With decreasing the attenuation, sharp multiple peaks appeared in the spectra below 30 dB. Such a split of spectrum is often observed when the feedback effect induces the amplitude modulation [5,6]. Figure 2(b) summarizes the dependence of normalized mean square error (NMSE) of NARMA2 task on the attenuation. It is clearly shown that NMSE can be minimized at an attenuation of 34 dB, below which the sharp multi peak structure appeared in the spectra. The observed minimum NMSE (6.4×10-6) is comparable to that theoretically obtained in the array of 16-coupled STOs [3]. This result indicates that single STO with delayed-feedback circuit can compete with the STO array. This work is supported by NEDO.

**References:**

[1] J. Torrejon et al., Nature 547, 428 (2017). [2] S. Tsunegi et al., Jpn. J. Appl. Phys. 57, 120307 (2018). [3] T. Kanao et al., Phys. Rev. Applied 12, 024052 (2019). [4] K. Fujii et al., Phys. Rev. Applied 8, 024030 (2017). [5] D. Kumar et al., Sci. Rep. 6, 30747 (2016). [6] T. Taniguchi et al., Phys. Rev. B 100, 174425 (2019). [7] J. Williame et al., Appl. Phys. Lett. 114, 232405 (2019). [8] M. Riou et al., Phys. Rev. Applied 12, 024049 (2019).

**Images:**

![](https://s3.eu-west-1.amazonaws.com/underline.prod/uploads/markdown_image/1/image/d49dc6b3239e7837d4a4db73ab12d4ba.jpg)
Schematic illustration of spin-torque oscillator (STO) with delayed-feedback circuit. Auto-oscillation is driven by random pulse voltage Vin(t). Power of the feedback is controlled by the rate of attenuator rAtt. Reservoir computing is performed using time-series amplitude Aout(t) obtained by the Hilbert transformation of output voltage Vout(t).

![](https://s3.eu-west-1.amazonaws.com/underline.prod/uploads/markdown_image/1/image/ae2ed6bb6522935b9ce2976e571c4ff4.jpg)
(a) Power spectral density (PSD) of STO with the delayed feedback circuit as a function of attenuation rate rAtt. (b) Normalized mean-squared error (NMSE) of second order nonlinear autoregressive moving average (NARMA2) task against attenuation rate. NMSE shows a minimum value (6.4×10-6) at rAtt = 34 dB.

 Enhanced Computational Ability of Spin Torque Oscillator with Delayed-Feedback Circuit for Physical Reservoir Computing

Recent research on deep learning shows that Binary Neural Network (BNN) is able to significantly reduce computation and memory cost with small precision loss [1][2]. By binarizing inputs and weights to +1 or -1 in BNNs, multiply-and-accumulate (MAC) operations can be replaced by XNOR operation in memory. Nonvolatile memory for BNN such as resistive random access memory (RRAM) and magnetic random access memory (MRAM) was reported in [3]-[6]. In this paper, we use spin-toque transfer magnetic random access memory (STT-MRAM) to complete XNOR and single-step convolution in memory. The main contributions include: (1) A STT-MRAM based structure with four transistors and two magnetic tunnel junctions (MTJ) (4T-2M) is proposed for binary convolution and fully connection operations using current mode sense amplifier. (2) The impact of different tunnel magnetoresistance ratio (TMR) and different size of MTJ on accumulating in memory has been explored. (3) By quantizing sensing yield into model, the trade-off between energy and performance is investigated. As shown in Fig. 1 (a), a 4T-2M bit-cell structure represents one synaptic weight, ‘-1’ is represented by two MTJs as parallel (P) and anti-parallel state (AP). The opposite combination is used to represent ‘1’. Two encoded word lines (WLs) represent one input value. To complete the XNOR operation, an offset-canceling current-sampling sense amplifier (OCCS SA) [7] is used (see Fig. 1(b)) to compare the current difference between source line (SL) and SLB. Fig.1 (c) shows the structure of the proposed STT-MRAM block. For convolution operation, the row decoder gated by appropriate timing decodes the multiple WLs to input parallel, and output through path &lt;1&gt; as the result of convolution. For fully connected operation, the row decoder activates the WLs row by row, and output through path &lt;2&gt; with bit-counting as the result of one fully connected. We implement 9 bits XNOR and accumulation to perform 3 × 3 convolution. Fig. 1(d) lists all the combinations in the convolution. The worst case is four ‘-1’ and five ‘1’ accumulation or its opposite combination, which causes the minimum current difference between “5IAP+4IP” and “5IP+4IAP”. Fig. 1(e) illustrates the circuit realization of BNN. When one group of WLs is simultaneously activated, ISL and ISLB will be different depending on the resistance of MTJs on two SLs. A CSA is implemented to generate high level when weighted sum is positive, and low level when weighted sum is negative. The output of the CSA represents the output of the neuron. SPICE-level simulation is performed using a Verilog-A model of STT MTJ and an industrial 28-nm CMOS process. Fig.2 (a) shows the simulation results that the sensing yield in different TMR and MTJ critical dimension (CD) can up to 99.8%. The sensing yield is positively correlated with TMR and negatively correlated with MTJ CD. As Fig.2(b) indicated, smaller MTJ CD leads to higher sensing margin [8]. As shown in Fig. 2(c), a BNN model consisting of two convolutional stages and one fully connected layer as the classifier is designed. Validation results on MNIST handwritten digit dataset is shown in Fig. 2(d). Sensing yield is quantized into model when sensing processes is failed, the results of convolution will be flipped. Lower TMR and larger MTJ CD result in a lower sensing yield and a dramatic decrease in accuracy. When TMR is greater than 2 with 40nm MTJ or TMR is greater than 1.5 with MTJ smaller than 35nm, the accuracy could achieve &gt;95% (&lt;1.8% loss). Fig. 2(e) illustrates the energy saving of single-step convolution can achieve 51.2% compared with that of row by row with a slight decrease in accuracy.

**References:**

[1] M. Courbariaux, et al., arXiv: 1602.02830, 2016. [2] M. Rastegari, et al., arXiv: 1603.05279, 2016. [3] Z. Dong et al., IEEE Transactions on Electron Devices, vol. 66, no. 1, pp. 793-801(2019) [4] L. Chang et al., IEEE Transactions on Very Large Scale Integration (VLSI) Systems, vol. 27, no. 11, pp. 2668-2679(2019) [5] Y. Pan et al., IEEE Transactions on Magnetics, vol. 54, no. 11, pp. 1-5(2018) [6] Y. Zhang et al., IEEE Transactions on Electron Devices, vol. 67, no. 2, pp. 469-473, Feb. 2020. [7] T. Na et al., IEEE Journal of Solid-State Circuits, vol. 52, no. 2, pp. 496-504, Feb. 2017. [8] Zhao, W. S., et al. Microelectronics Reliability, vol. 52, no. 9–10, Sept. 2012.

**Images:**

![](https://s3.eu-west-1.amazonaws.com/underline.prod/uploads/markdown_image/1/image/946bef43ce91a98b12b19a0fcba53e6c.jpg)
Fig.1 (a) Cell configurations to represent one weight using two MTJs and four transistors. (b) Schematic of OCCS SA. (c) 4T2M based array to complete convolution and fully connection, <1> indicates path 1 and <2> indicates path 2. (d)The list of all the combinations in the convolution. (e) Schematic of the XNOR and accumulate operation as convolution in memory.

![](https://s3.eu-west-1.amazonaws.com/underline.prod/uploads/markdown_image/1/image/f8614fa534b410c9fbcbfa4ee258bd1b.jpg)
Fig.2 (a) The sensing yield in different TMR with different MTJ CDs. (b) The resistance distribution of different MTJ CDs. (c) Diagram of the BNN topology used for evaluation with MNIST. (d) The performance of binary neural network with different MTJ CDs and different TMR. (e) Trade-off between energy saving and accuracy loss.

 In-MRAM Processing Elements with Single-Step Convolution for Binary Neural Network

Classifying or predicting complex time-dependent signals (e.g. speech, financial data, the weather) is a challenging computational task. Reservoir computing (RC) is an efficient neuromorphic computing approach that is ideally suited to such tasks and is typically implemented in software using a recurrent neural network (RNN) with fixed synaptic weights (the reservoir) connected to a single, trainable readout layer. However, more efficient implementations of RC are possible if the software RNN reservoir is substituted with a physical system with the correct properties, such as non-linear response to input signals and inherent memory, leading to a readily deployable, hardware-based neuromorphic computing platform [1]. In this work, we propose a novel approach to RC where the dynamics of a single magnetic domain wall (DW) trapped between two defect sites in a nanostrip acts as a hardware-based reservoir. We demonstrate how such a device, with dimensions smaller than 1 µm, is capable of performing complex data analysis tasks, such as speech recognition. We have modelled a Ni nanowire with two anti-notches (shown in the inset of Fig. 1(a)) using both a simple 1D model[2] and micromagnetic simulations. Both models show the DW exhibit complex oscillatory dynamics similar to the Duffing oscillator, thus giving highly non-linear responses to applied magnetic fields (Fig. 1(a)). We exploit the DW dynamics for RC by using an applied field to inject time-multiplexed input signals into the reservoir and show how this approach allows the device to perform classification tasks. We have explored how the regime of applied fields affects the accuracy of sine and square wave classification, showing that the best recognition rate is obtained at the edge of a chaotic regime of oscillation (Fig. 1(b)). We have also demonstrated that the same approach can be used for more complex tasks, such as spoken digit recognition and handwritten digits recognition. Our work opens a new perspective for neuromorphic computing in nanomagnetic hardware.

**References:**

[1] M. Riou et al., Phys. Review Applied,2019 [2] A. Pivano et al., Phys Review B, 2016

**Images:**

![](https://s3.eu-west-1.amazonaws.com/underline.prod/uploads/markdown_image/1/image/947f0ad21dbe4e988fab9e7c037ab764.jpg)
Fig. 1: Simulations of Ni nanostrip at 500 MHz. Figure (a) shows a typical bifurcation diagram computed with 1D model at 500MHz and the inset of Fig. 1(a) illustrates the typical geometry of a Ni nano-strip with two anti-nothces separated by 350 nm. Figure (b) shows the recognition rate for the sine and square task computed with 1D model of a domain wall in a Ni nanostrip. The input field is oscillatory comprising two terms: H-the constant offset of the field and ΔH-the amplitude of the stimulus. The inset shows a sequence of inputs and its corresponding output calculated during the classification process using 1D model.

 Neuromorphic computation with a single magnetic domain wall

Unconventional computing utilizing stochastic behavior to effectively solve complex problems, i.e., "probabilistic computing," has gathered much attention [1]. Stochastic magnetic tunnel junction (MTJ) is a promising device to compose the probabilistic bit, a fundamental unit of the probabilistic computer, with efficient as well as individual control of its stochasticity. The average duration of MTJ dwelling in each state, i.e., relaxation time τ, determines the critical indices of the computer's performance, e.g., accuracy and computing time. As a proof of concept, integer factorization has been demonstrated with stochastic MTJs with a perpendicular easy axis (p-MTJs) [2], whose typical τ is on the order of milliseconds. Meanwhile, τ of MTJs with an in-plane magnetic easy axis (i-MTJs) is reported down to sub-microseconds [3]. Here we experimentally and theoretically address the τ of nanomagnets in low-thermal-stability MTJs and elucidate the physical mechanism causing the different fluctuation timescale between p- and i-MTJs. A stack structure, Ta (5.0)/ PtMn (20)/ Co (2.6)/ Ru (0.90)/ CoFeB (2.4)/ MgO/ CoFeB (2.1)/ Ta (5.0)/ Ru (5.0) (thickness in nm), is deposited on a thermally oxidized Si substrate by dc/rf magnetron sputtering. Both bottom (reference) and top (free) CoFeB layers have an in-plane easy axis. Effective perpendicular anisotropy field μ0HKeff of the top CoFeB layer is -0.46 T (negative sign indicates in-plane easy axis). The stack is processed into elliptic MTJs with geometry averaged diameter D of 55 nm. Typical TMR ratio is 110% and RA = 32 Ωμm2. Figure 1(a) shows the measurement circuit and Fig. 1(b) shows time-averaged 〈R〉 as a function of in-plane magnetic field H along a long axis of MTJs, indicating superparamagnetic behavior with zero coercivity. Figure 1(c) shows a measured random telegraph noise (RTN) at μ0H = -7.9 mT. τ averaged for P and AP states τave = (τPτAP)0.5, where τP (τAP) is the τ to stay at P (AP) state, is 19 ns, which is about two orders shorter than that ever reported in MTJs [3]. We then numerically calculate τ of p- and i-MTJs based on a stochastic Landau-Lifshitz-Gilbert (LLG) equation including a thermal field [4]. We define magnetic energy with HKeff and in-plane anisotropy field HK,in as E = MSv(HKeff+HK,insin2φ)sin2θ/2, with MS, v, θ, and φ being the spontaneous magnetization, the volume of nanomagnet, the polar angle, and the azimuthal angle, respectively. We set the same Δ for both systems; μ0HKeff = 10 mT, α = 0.02, and thermal stability factor Δ = HKeffMSv/2kBT = 3.8 (kBT is thermal energy) are assumed for the p-MTJ, and μ0HKeff = -0.46 T, μ0HK,in = 10 mT, α = 0.02, and Δ = HK,inMSv/2kBT = 3.8 for the i-MTJ. Figures 2(a) and 2(d) show the directions of magnetization, a torque from perpendicular magnetic anisotropy, that by damping, and that by the thermal fluctuation calculated by E and LLG equation for p- and i-MTJs, respectively. In the p-MTJs, the torque from HKeff directs in the azimuthal direction, and the only torque directing in the polar direction to induce magnetization switching is the thermal fluctuation field. Accordingly, the magnetization precesses with perpendicular anisotropy field and switches from one energy stable direction to the other with a random fluctuation field, as shown in the magnetization trajectory in Fig. 2(b). In this case, τ is found to be well described by the random-walk under damping [4,5] as τ = π0.5Δ-0.5(2αγμ0HKeff)-1exp(Δ). τ in this condition is calculated as ≈ 0.8 ns, which is consistent with the calculated RTN shown in Fig. 2(c). On the other hand, as shown in Figs. 2(d) and 2(e), the directions of the torques in i-MTJs are different from those in the perpendicular system due to the difference in the landscape of energy potential. τ is calculated to be 20 ns [Fig. 2(f)], which is close to the experimentally obtained τ, and several tens of times shorter than that calculated for the perpendicular system even with the same Δ. We numerically simulate τ as a function of Δ and clarify that the values are well described by τ = π(αγμ0Δ)-1[HKeff(HKeff+HK,in)]-0.5exp(Δ), which is analytically derived with Fokker-Planck equation [4,6] under Δ » 1 and -HKeff » HK,in. In conventional understanding, a decrease of HKeff decreases Δ, leading to shorter τ for p-MTJs. However, our analysis indicates that it increases the prefactor of exp(Δ), in proportion to HKeff -1.5. This dilemma determines a lower limit of τ as τmin = MSvln2(αγμ0kBT)-1, at which magnetization dynamics is dictated by the thermal fluctuation. For example, τmin is 151 ns for a p-MTJ with D = 60 nm, thickness of 1 nm, MS = 1.4 T, and α = 0.02 at room temperature. For i-MTJs, one can be free from the dilemma because the prefactor is mainly determined by HKeff whereas exp(Δ) is determined by HK,in, leading to (πΔ)-0.5MS|HKeff|v/4kBT times shorter τ than that with p-MTJs for the same Δ. This ratio reasonably explains the different τ observed for p-MTJs and i-MTJs, indicating i-MTJs with μ0|HKeff| ~ 1 T can achieve τ down to nanoseconds. This work is in part supported by JST-CREST No. JP1082016, and JSPS Nos. 19J12206, 19J12926, 19H05622, 20H02178, and 19KK0130.

**References:**

[1] P. R. Feynman, Int. J. Theor. Phys. Vol. 21, p.467-488 (1982) [2] W. A. Borders, A. Z. Pervaiz, S. Fukami, et al., Nature Vol. 573, p.390-393 (2019) [3] B. Parks, A. Abdelgawad, T. Wong, et al., Phys. Rev. Appl. Vol. 13, p.014063 (2020) [4] W. F. Brown Jr., Phys. Rev. Vol. 130, p.1677-1686 (1963) [5] T. Taniguchi, and H. Imamura, Phys. Rev. B Vol. 85, p.184403 (2012) [6] T. Taniguchi, Y. Utsumi, and H. Imamura, Phys. Rev. B Vol. 88, p.214414 (2013)

**Images:**

![](https://s3.eu-west-1.amazonaws.com/underline.prod/uploads/markdown_image/1/image/d931fe36020e94e2cb208537fe219b0d.jpg)
Fig. 1 (a) Measurement circuit for time-averaged resistance 〈R〉 and random telegraph noise (RTN). (b) In-plane magnetic field μ0H vs. 〈R〉. (c) RTN signal measured as transmitted voltage VT.

![](https://s3.eu-west-1.amazonaws.com/underline.prod/uploads/markdown_image/1/image/3dbfad6da1f9511ba98d94f34ea33a69.jpg)
Fig. 2 (a)(d) Directions of the torques. (b)(e) Calculated magnetization trajectories and (c)(f) time vs. their easy axis component. The conditions for (a)-(c) perpendicular and (d)-(f) in-plane easy axes are shown.

 Superiority of In-Plane Easy-Axis Stochastic Nanomagnet for Shorter Relaxation Time

The bottleneck between data storage and processing on conventional computing hardware is seen as a significant limitation to many forms of machine learning algorithms [1]. One route to solve this issue is to employ physical devices that can naturally mimic brain-like behaviour, known as neuromorphic computing. Magnetic materials are already widely used for long-term data storage, but ongoing work seeks to make use of their potential as both working memory and computing architectures. Particularly, devices based on magnetic domain walls (DWs) can perform logic operations [2] and can readily store information [3]. However, the stochasticity of DW pinning limits the feasibility of creating technologically viable devices. Here, we demonstrate how stochasticity can be changed from a technologically inhibitive behaviour into an integral property of machine learning algorithms that could be used in specialised neuromorphic devices. We first present experimental measurements that demonstrate the feasibility of tuning stochastic processes by applying external stimuli. Focused magneto-optic Kerr effect measurements were used to probe the pinning of DWs at notch-shaped defect sites in 400 nm wide Permalloy nanowires (Fig. 1(a)). DWs were injected into the nanowires and propagated to the defect sites using a field parallel to the nanowire (Hx). The probability of the DWs being pinned at the defect sites was found to depend sigmoidally on the magnitude of a field transverse to the nanowire length (Hy), (Fig. 1(b)). We apply this stochastic behaviour in two possible contexts, as a stochastic neuron for a binary network or as a stochastic synapse for feed forward network, where we model the pinning behaviour based on the experimental results. In the first case, we have modelled these DW wires as neurons in a Boltzmann machine, which exploits their randomness in order to sample a defined, or trained, probability distribution. We apply this to the task of integer factorisation [4], where the connectivity of the neurons is defined so that the binary representation of the integer factors are the most probable states. Such a device inherently exploits the tunable stochasticity of the system to solve complex problems. In the second case, we have explored the properties of feedforward artificial neural networks where the nanowires acted as binary stochastic synapses (BSS). These synapses are individually random but the output can be improved by repeatedly sampling the network to include redundancy. We have developed both mean-field and stochastic based learning methods, where for low repeats of the synapse the stochastic learning is seen to outperform the mean-field based method. It is found that for a single cycle of the network it can be trained to recognise handwritten digits with error rate of 17%. Repeated sampling of the network decreased the error rate to 8% for 128 cycles - close to mean field performance (Fig. 2). Following this we have expanded our network to become a multi-layer perceptron with both inhibitory and excitatory synapses. The additional layer increases the pathways through the network thus reducing the impact of the stochasticity on the classification. For the same classification test the error rate is seen to reduce to 5.5% with a hidden layer of 500 neurons. In conclusion, the pinning of DWs in nanowires can be tuned by applying a magnetic field transverse to the wire and the resultant pinning probability has been used as both stochastic neurons and synapses. As a neuron, we have demonstrated its ability to perform integer factorisation as part of a Boltzmann machine. While as a stochastic synapse, we have been able to train single and multi-layer networks to classify handwritten digits with close to digital network performance. Our work illustrates how the intrinsic stochasticity of DW devices can be harnessed to provide tunable bespoke hardware for machine learning tasks.

**References:**

[1] M. M. Sabry Aly et al., Proceedings of the IEEE, Vol. 107, no. 1, p. 19 (2019) [2] D. A. Allwood et al., Science, Vol. 309, Issue 5741, p. 1688 (2005) [3] R. L. Stamps et al., J. Phys. D. Appl. Phys., Vol. 47, p. 333001 (2014) [4] W. A. Borders et al., Nature, Vo. 573, p. 390 (2019)

**Images:**

![](https://s3.eu-west-1.amazonaws.com/underline.prod/uploads/markdown_image/1/image/488de22bf5135fdfd14a1e4eeaa1d155.jpg)
Fig. 1: (a) Schematic of a notched magnetic nanowire for use as a synapse with a parallel driving field (Hx) and a tunable transverse depinning field (Hy). (b) Pinning probability as a function of transverse field, showing a sigmoidal dependence.

![](https://s3.eu-west-1.amazonaws.com/underline.prod/uploads/markdown_image/1/image/0c39f92c401e21de2e6218da03982401.jpg)
Fig. 2: The validation error rate of handwritten digits using a single layer network with binary stochastic synapses (BSS) for varying number of cycles of the network. Lines and points show the training using the mean-field or stochastic methods respectively.

 Machine Learning with Stochastic Magnetic Domain Wall Based Neurons and Synapses

Magnetic tunnel junctions consist of two ferromagnetic layers separated by a thin insulating layer. The relative orientation of the two layers' magnetization has two stable configurations: parallel or antiparallel (P or AP). For use as a binary non-volatile memory in magnetic random access memory (MRAM), the retention time in each of the two configurations needs to be on the order of several years. This corresponds to energy barriers larger than 40 kT (k is the Boltzmann constant, and T=300 K is room temperature). If the energy barrier becomes smaller than 12 kT, thermal fluctuations induce random switching of the magnetization between the P and the AP states with a mean timescale between 10 µs and 1 ms. Such fluctuating, low-energy-barrier tunnel junctions are referred to as superparamagnetic tunnel junctions [1]. Recently, superparamagnetic tunnel junctions have become attractive as compact and energy-efficient devices for applications that require truly random variables [2,3], including innovative computing schemes [4,5,6,7,8]. Through adapted circuit co-design approaches, we previously showed that those devices are promising for implementing artificial neural networks with reduced energy consumption [9]. In cognitive computing, these superparamagnetic tunnel junctions can be used to emulate biological neurons [10, 11]. Here, we investigate simple ways of coupling these devices to extend these innovative computing schemes. To mimic neuron-like interconnections, it is important to develop simple schemes to couple such devices. Significant work has been done to develop coupling strategies for magnetic systems such as harmonic spin-torque nano-oscillators, which are useful for cognitive computing [12,13,14,15], but there is no equivalent scheme for superparamagnetic tunnel junctions. In this work, we experimentally demonstrate a simple electrical coupling method between two superparamagnetic tunnel junctions. We couple two stochastic magnetic junctions by placing them in parallel in a simple electrical circuit. We use the fact that their switching rate can be controlled deterministically by the applied current. They couple because their stochastic electrical transitions change the current flowing through the other device, changing its switching rate. In our circuit, the total applied current in each nanojunction is shared and depends on the joint resistance states of the nanojunctions. Depending on the current state of the system, transitions to certain joint states are favored while others are less probable. This coupling mechanism leads to non-zero cross-correlation between the stochastic switching events of the two fluctuating tunnel junctions, demonstrating their mutual coupling. We show experimentally that the cross-correlation becomes large when the switching time scales of the two superparamagnetic tunnel junctions are such that they both have roughly a 50 % probability to be in the parallel or antiparallel state. We also demonstrate that the cross-correlation increases when the total current applied to the system increases. We reach 18 % for the largest Pearson correlation value measured in the system. We find good agreements between numerical simulations of a comparable four-state Markov state process and our experiments. As we do not use sophisticated circuits for the coupling purpose, our approach opens new paths to compact and energy-efficient implementations of scaled-up arrays of coupled superparamagnetic tunnel junctions for new cognitive computing schemes.

**References:**

[1] W. H. Rippard et. al. PRB, Vol 84, 064439 (2011). [2] B. Parks et. al. AIP Advances, Vol 8, 055903 (2018). [3] D. Vodenicarevic et. al. Phys. Rev. Appl, Vol 8, 054045 (2020). [4] A. Mizrahi et. al. Nature Communications, Vol 9, 1533 (2018). [5] K. Camsari et. al. Appl. Phys. Rev, Vol 6, 011305 (2019). [6] K. Camsari et. al. Phys. Rev. X, Vol 7, 031014 (2017). [7] A. Sengupta et. al. IEEE Trans. on Electron Devices, Vol 63, 2963 (2016). [8] W. A. Borders et. al. Nature, Vol 573, 390 (2019). [9] M. W. Daniels, et al. Phys. Rev. Appl, Vol 13, 034016 (2020). [10] W. Gerstner et. al. Neuronal Dynamics, Cambridge Univ. Press (2014). [11] J. Grollier et. al. Nature Electronics, Vol 3, 360 (2020). [12] W. H. Rippard et. al. PRL, Vol 95, 067203 (2005). [13] S. Kaka et. al. Nature, Vol 437, 389 (2005). [14] A. A. Awad, Nat. Phys, Vol 13, 292 (2017). [15] M. Romera†, P. Talatchian† et al. Nature, Vol 563, 230-234 (2018).

**Images:**

 Mutually coupled superparamagnetic tunnel junctions

The end of Moore’s Law for complementary metal-oxide semiconductor (CMOS) technology has prompted the search for low-power computing, resulting in several promising proposals based on magnetic logic. One approach aims at tailoring arrays of nanomagnetic islands in which the magnetostatic interactions constrain the equilibrium orientation of the magnetization to embed logical functionalities [1]. Despite the realization of several proofs of concepts of such nanomagnetic logic, it is still unclear what the advantages are compared to the widespread CMOS designs, due to their need for clocking and/or thermal annealing for which fast convergence to the ground state is not guaranteed. In fact, it seems increasingly evident that “beyond CMOS” technology will require a fundamental rethinking of our computing paradigm. In this respect, digital memcomputing machines have been proposed [2]. These machines consist of self-organizing logic gates (SOLGs) which are able to dynamically “self-organize” into their logically correct states irrespective of whether the signal is applied to the traditional input terminals, or the traditional output terminals. This type of terminal-agnostic logic thus allows for reversed computing. Here [3], we introduce a novel concept of SOLGs that employs stray-field coupled nanomagnetic islands to perform terminal-agnostic logic: nanomagnetic self-organizing logic gates. We thereby focus on the design of a functionally complete NAND gate with which any Boolean circuit can be constructed in a bottom-up approach. Our strategy to build such a nanomagnetic SO-NAND gate relies on two main properties: balancedness and dynamic error suppression (DES) – see Figure 1. For reversible logic to work, the logically correct states should be occupied equally in thermodynamical equilibrium (and not only after a relaxation process into the ground states, as balancedness was originally defined [4]). This contrasts the balanced NAND gate proposed in Ref. [5]. To allow for larger circuits, the probability of being in a logically incorrect state should suppressed. To this end, our DES scheme periodically checks the logical correctness of the gate/coupling and applies additional fields on each island of the gate/coupling if necessary. Our DES scheme is thus based on local information, i.e. the magnetization state of a single gate or coupling. This locality is in sharp contrast to the biasing used in p-bit logic inversion, for which the logical behavior of a collection of super-paramagnetic tunnel junctions (sMTJ) is fully imposed by globally-determined time-varying spin currents [6]. Owing to its locality, our approach does not suffer from scaling issues. To show the usefulness of our SO-NAND gate, we use it as a building block to construct a self-organizing two-bit multiplier – see Figure 2. Our SO-2BM is capable of decomposing any output number into its (prime)factors, finding each possible decomposition without clearly favoring nor penalizing any solution. This fact indicates that the balancedness remains largely conserved throughout the whole system, which allows the extension to larger systems in order to solve more complex problems.

**References:**

[1] A. Imre, G. Csaba, L. Ji et al., Science, Vol. 311, p. 205-208 (2006). [2] F. Traversa and M. Di Ventra, Chaos, Vol. 27, p. 023107 (2017). [3] P. Gypens, J. Leliaert, M. Di Ventra et al., arXiv preprint arXiv:2012.12721 (2020). [4] J. Lust and B. Dixon, Inf. Sci., Vol. 113, p. 193-204 (1999). [5] P. Gypens, J. Leliaert and B. Van Waeyenberge, Phys. Rev. Appl., Vol. 9, p. 034004 (2018). [6] W. Borders, A. Pervaiz, S. Fukami et al., Nature, Vol. 573, p. 390-393 (2019). [7] J. Leliaert, A. Vansteenkiste, A. Coene et al., Med. Biol. Eng. Comput., Vol. 53, p. 309-317 (2015).

**Images:**

![](https://s3.eu-west-1.amazonaws.com/underline.prod/uploads/markdown_image/1/image/7d77a99461c449a155c9bf16943ac1b1.jpg)
Fig. 1: Schematic representation of a self-organizing NAND gate consisting of four nanomagnetic islands with perpendicular anisotropy. The probability distribution is obtained with the macrospin simulation tool Vinamax [7] which numerically solves the Landau-Lifshitz-Gilbert equation taking thermal fluctuations into account. Logically incorrect states are suppressed by a dynamic error suppression (DES) scheme, while still preserving the balancedness of the gate. Prel is the probability relative to the most likely logical state.

![](https://s3.eu-west-1.amazonaws.com/underline.prod/uploads/markdown_image/1/image/bdd9a7d3218e170d709dc62ca31af92c.jpg)
Fig. 2: Schematic representation of a self-organizing two-bit multiplier and the relative probability distribution obtained with Vinamax for an output fixed to 0. Prel is the probability relative to the most likely logical state. The four output islands of the SO-2BM, labeled from a to d, are fixed such that they represent a certain number (0, 1, 2, 3, 4, 6, or 9) and the system is allowed to explore its state space. The magnetization of the input islands, corresponding to the solution of the computation, are labeled from A to D. All states of which one of the inputs is logically inconsistent are aggregated into the "??"-state.

 Nanomagnetic Self-Organizing Logic Gates

The topological charge or skyrmion number Q is a useful measure to characterize the topology of spin textures such as vortices and skyrmions in two-dimensional systems. When the magnetization field m(r) is projected onto the unit sphere, Q measures the number of times the magnetic moments wrap around this sphere. For vortices and merons, Q = ±1/2, while for skyrmions, Q = ±1. Higher-order half- and full-integer charges are also possible. In numerical micromagnetism, a common approach involves solving the equations of motions for m(r) that is discretized on a regular grid. The underlying assumption is that cell-to-cell variations in m are sufficiently small such that the exchange energy, proportional to the square of the magnetization gradient, remains meaningful. However, issues can arise under certain conditions, such as in the nucleation and annihilation of vortices and skyrmions, or in the stochastic dynamics under random fields, where large cell-to-cell variations in the magnetization can reduce the accuracy of the finite-difference approximations of the spatial derivatives used to estimate Q, which can result in spurious deviations from half- and full-integer values of Q. Here, we will describe a lattice-based implementation for the open source micromagnetics code MuMax3 [1] that allows an accurate determination of Q, particularly under Langevin dynamics used to model finite temperatures and in nucleation and annihilation processes [2]. The implementation is based on the approach of Berg and Lüscher [3], which involves summing over the solid angle subtended by three spins, forming elementary signed triangles, that are constructed from the finite difference cells. The implementation accounts for both periodic boundary conditions and finite-sized systems. A comparison between the finite-difference and lattice-based approaches is given in Fig. 1, where the time variation of the total topological charge is shown for a system hosting a single skyrmion under a temperature of 300 K. The blue circles represent the Q computed using finite-difference derivatives, which exhibit large fluctuations about the expected value of Q = -1 that are about as large as Q itself. Moreover, the time-averaged mean Q does not coincide with -1. The solid red line represents the result of the lattice-based approach, which remains constant near the expected value of -1. More precisely, the result is limited primarily by the single-precision of the floating-point arithmetic used, e.g., we find Q = −1.0000001, −1.0000004, −1.0000008, −1.0000002, and −0.9999996 over 20-ns intervals at 400 K. We also show how the lattice-based approach is vastly superior to the finite-difference approximation in other test cases, which involve skyrmion-antiskyrmion pair nucleation due to spin-transfer torques and skyrmion fluctuations in finite dots. While the results do not necessarily call into question the validity of published work (since the topological charge is often only used as a proxy for magnetization gradients), they do highlight the care with which noninteger values of Q should be interpreted, particularly when processes such as nucleation, annihilation, and thermal fluctuations are at play. This work was partially supported by the Agence Nationale de la Recherche under contract no. ANR-17-CE24-0025 (TOPSKY) and Fonds WetenschappelijkOnderzoek (FWO-Vlaanderen) through Project No. G098917N.

**References:**

[1] A. Vansteenkiste, J. Leliaert, M. Dvornik et al., AIP Adv 4, 107133 (2014). [2] J.-V. Kim and J. Mulkers, IOP SciNotes 1, 025211 (2020). [3] B. Berg and M. Lüscher, Nucl Phys 190, 412 (1981).

**Images:**

![](https://s3.eu-west-1.amazonaws.com/underline.prod/uploads/markdown_image/1/image/d45266e543d925ddaf2fecf323e4aa0c.jpg)
Topological charge, Q, as a function of time for a skyrmion in a 1-nm thick film with periodic boundary conditions at 300 K. Blue circles represent the calculation based on a finite-difference approximation to spatial derivatives, as provided by the package ext_topologicalcharge in MuMax3, while the solid red line represents the calculation with the lattice-based approach, which is provided by the package ext_topologicalchargelattice. The top inset illustrates snapshots of the micromagnetic state.

Entropic effects and solitons in thermally activated magnetic transitions INVITED

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