Domain wall (DW) devices are gaining additional attention for their suitability in emerging neuromorphic computing (NC) 1. Underlying building blocks of NC are neurons and synapses, where neurons process the information and synapses control the information passing from pre-neurons to post-neurons. Such synaptic functions can be achieved by realizing multiple conductance (resistance) states. In DW devices, this virtue can be achieved by controllably stopping the DW at multiple specific positions. However, DW motion is stochastic, and therefore, there is a need to artificially form centers by amending magnetic/geometric properties that can controllably pin the DWs 2.
In the present study, we have proposed and studied the concept of nanoscale interfacial Dzyaloshinskii-Moriya interaction (iDMI) region to pin the DW. When an electrical current is passed through the ferromagnetic (FM) nanowire, the DW moves from the left end of the wire and gets pinned at the pinning site for a large range of iDMI constant, the width of the pinning site, and current density (J) values. The DWs can be depinned from the pinning site by applying a higher J. We found that local iDMI forms a barrier in energy locally and hence pins the DW. Besides, the magnetostatic interaction between DW and the pinning site plays an important role in pinning the DWs. In addition to DW pinning, we observed that the DW oscillates at GHz frequencies at the pinning site 3. Later, we introduced five pinning sites along the FM wire and observed that the DW can be pinned (and depinned) successively at every pinning site. Therefore, multiple resistance states and hence synaptic operations can be achieved. The results of this study will be discussed in full detail during the conference. 


References 
1. A. Sengupta and K. Roy, Applied Physics Express, 11 (2018) 030101. 
2. D. Kumar et al., IEEE Transactions on Magnetics, 55 (2018) 1-8. 
3. D. Kumar et al., Journal of Magnetism and Magnetic Materials, (2020) 166807.

Domain wall pinning through nanoscale interfacial Dzyaloshinskii-Moriya interaction

Demand for Artificial Intelligence (AI) has been growing due to their usefulness in commercial electronics and in tackling global issues, such as a pandemic. However, conventional von Neumann devices that use AI require extensive programming and power. Thus, significant attention is dedicated to the brain-inspired neuromorphic computing (NC) as a more power-efficient alternative and a suitable platform for AI. NC involves a network of interconnected synapses and neurons that mimic their biological counterparts. In this work, we present a DW-based artificial neuron that automatically resets without applying current.
We simulated a synthetic antiferromagnetic (SAF) DW device where the free layer (CoFeB) is driven by spin-transfer-torque (STT), achieving the integrate function. In the absence of current, the antiferromagnetic coupling (AFC) between the free and pinned layers pushes the DW back towards the starting position. This happens automatically, and is indicative of the leakage function. The integrate and leakage functions are also achieved experimentally, suggesting that an SAF-based neuron is viable for NC.

Synthetic antiferromagnetic domain wall device as artificial neuron for neuromorphic computing

Demand for Artificial Intelligence (AI) has been growing due to their usefulness in commercial electronics as well as tackling global issues, such as a pandemic [1]. Current devices that perform AI rely on the conventional von Neumann architecture, which requires extensive programming and power [2]. Therefore, significant attention is dedicated to the brain-inspired neuromorphic computing (NC) as an alternative that is more power-efficient and a more suitable platform for AI [3]. Neuromorphic architecture consists of a network of interconnected synapses and neurons that mimic their biological counterparts [4]. Sengupta et al. have proposed a neuron design based on a domain wall device that uses a magnetic tunnel junction (MTJ) [5] at one end. The tunneling magnetoresistance (TMR) depends on the position of the DW. A current is required to move the domain wall forward or backward (integrate or leak functions respectively). In this work, we place emphasis on designing a domain wall (DW) based artificial neuron, which displays the leaky function without the requirement for a driving current. To achieve leaky function and self-reset process of neuron, we propose a domain wall device, which has an anisotropy field (Hk) gradient in the region of interest (ROI), as shown in Fig. 1(a). This anisotropy gradient is crucial in inducing the automatic returning motion of DW in the absence of current. The design is simulated using Mumax3, and the DW in the free layer nanowire (NW) is driven by spin-transfer-torque (STT). As shown in Fig. 1, current is first applied to propagate the DW towards the threshold position to “fire” the neuron device; current is subsequently removed and DW motion is observed. The micromagnetic parameters used here represent the magnetic properties of CoFeB film with a perpendicular magnetic anisotropy. The magnetic anisotropy constant (Ku) and saturation magnetization (Ms) are graded in ROI, so that Hk gradient is achieved. Some results are presented in Fig. 2(b), showing DW position vs time graph of the reference NW, without any gradient. One may notice that the DW does not return towards the initial position in these devices. Fig. 2(c) corresponds to a device with an Hk gradient. We can notice that the device enables natural return of the domain wall towards the starting position in the absence of current. This suggests that the device displays the leakage function, as well as the ability to reset after the neuron has fired. Fig. 2(d) corresponds to the same device as that of Fig. 2(c), except in this case, three current pulses are consecutively applied with a time interval of 60 ns. These results indicate that the neurons can function with a reset time of about 60 ns. The proposed design has potential applications for DW-based artificial neuron since it displays the integration and leakage functions, while the firing function can be achieved with the utilization of MTJ during the fabrication process.

**References:**

[1] Z. Hu, Q. Ge, S. Li, L. Jin, and M. Xiong, “Artificial Intelligence Forecasting of Covid-19 in China,” Feb. 2020, Accessed: Jun. 25, 2020. [Online]. Available: http://arxiv.org/abs/2002.07112. [2] R. A. Nawrocki, R. M. Voyles, and S. E. Shaheen, “A Mini Review of Neuromorphic Architectures and Implementations,” IEEE Trans. Electron Devices, vol. 63, no. 10, pp. 3819–3829, 2016, doi: 10.1109/TED.2016.2598413. [3] H. Farkhani, M. Tohidi, S. Farkhani, J. K. Madsen, and F. Moradi, “A Low-Power High-Speed Spintronics-Based Neuromorphic Computing System Using Real-Time Tracking Method,” IEEE J. Emerg. Sel. Top. Circuits Syst., vol. 8, no. 3, pp. 627–638, Sep. 2018, doi: 10.1109/JETCAS.2018.2813389. [4] J. Grollier, D. Querlioz, K. Y. Camsari, K. Everschor-Sitte, S. Fukami, and M. D. Stiles, “Neuromorphic spintronics,” Nat. Electron., 2020, doi: 10.1038/s41928-019-0360-9. [5] A. Sengupta, P. Panda, P. Wijesinghe, Y. Kim, and K. Roy, “Magnetic tunnel junction mimics stochastic cortical spiking neurons,” Sci. Rep., vol. 6, no. July, pp. 1–9, 2016, doi: 10.1038/srep30039.

**Images:**

![](https://assets.underline.io/uploads/markdown_image/1/image/e015896fad0991ac5a8452eee8bf2d23.jpg)
The proposed 3T-MTJ device. Magnetizations of the pinned layers are fixed while that of the MTJ free layer NW can be manipulated by applying electrical current. Conduction electrons are polarized by a pinned layer and push the DW by STT. Neuronal firing happens when the DW propagates past the MTJ device, i.e. when parallel configuration of the MTJ is achieved.

![](https://assets.underline.io/uploads/markdown_image/1/image/e66f29e5f0d82f3c0bbcc2c2314dc9be.jpg)
(a) Schematic of the NW with the ROI labelled. (b and c) DW position vs time graphs for the reference NW and the result where DWs returned and stabilized around the initial position after neuronal firing. (d) is the case with three pulses sequentially applied. The interval is the same between each pulse, sufficient for DWs to stabilize around the initial position.

 Domain wall device with a graded anisotropy field as artificial neuron for neuromorphic computing

Human brain-inspired neuromorphic computing (NC), which emulates neural activities, is considered for the low-power implementation of artificial intelligence. It gives the benefit of parallel processing, ultralow power consumption, self-learning, and fault tolerance. The realization of hardware elements, such as artificial synaptic devices, which are the basic building blocks of neuromorphic computing systems, is an essential path towards this goal. Two terminal artificial synaptic devices have been studied using resistive random-access memory and memristors and have emulated the synaptic functions. However, in these two-terminal devices, both learning and signal transmission cannot occur concurrently, which leads to an incomplete emulation of a synapse. On the other hand, in a three-terminal synaptic device, signal transmission and learning occur independently through the channel and external gate respectively, leading to complete emulation of a synapse. Much progress has been made in three-terminal devices based on oxides [1], organic materials [2,3], and 2D materials [4]. Electrolyte gating has been widely used for conductance modulation by massive carrier injections at a very low gate voltage. However, studies based on metallic ferromagnetic systems remain highly unexplored. Additionally, a synaptic device based on magnetic metal has a spin degree of freedom that can be tuned along with. In this work, we have demonstrated a three-terminal coplanar synaptic transistor based on permalloy by electrolyte gating. The channel consists of a stack of Ta (3 nm)/NiFe (3 nm)/Ta (1 nm) on the silicon substrate. EMIM-TFSI ionic liquid was used as the gate dielectric. Fig. 1(a) shows the transfer curve measured at different sweeping rates. A giant anticlockwise loop opening in the transfer curve shows a non-volatile and reversible change of channel conductance suitable for emulating synaptic functions. We have obtained the highest room temperature conductance modulation of 11 % for a metal at the slowest sweeping rate, as shown in Fig. 1(b). The transistor's working principle is based on the formation of an electric double layer (EDL) at the interface of ionic liquid and channel and by intercalation/ extraction of oxygen ions. Multilevel non-volatile states are a prerequisite for realizing analog computing. We have obtained gating controlled multilevel, non-volatile, reversible conducting states by applying positive gate pulses of different amplitude shown in Fig. 1(c). The channel conductance remained constant at zero gating showing the non-volatile nature of the state. Furthermore, the channel conductance can be brought back to the initial state by applying negative gate pulses. The retention of conducting states is checked separately, which shows retention of up to a few hours or more. In this permalloy based artificial synapse, the gate pulses act as the external stimuli, and the channel conductance act as the synaptic weight. We have emulated the essential synaptic functions at room temperature. A basic form of short-term plasticity like excitatory postsynaptic conductance (EPSC) and paired-pulse facilitation (PPF) has been emulated, as shown in Fig. 2(a) and Fig. 2(b), respectively. Moreover, we have shown a transition from short-term memory (STM) to long-term memory (LTM) by varying the gate pulse amplitude, duration, and number shown in Fig. 2(c). The change in channel conductance is more significant at higher amplitude, duration, and number due to the intercalation/extraction of more oxygen ions into the channel. Long-term plasticity behavior like long-term potentiation (LTP) and multiple potentiation and depression cycles have been demonstrated in Fig. 2(d)-(f). The writing and reading energy per synaptic event are calculated at different gate amplitude and duration showing a linear escalation. The lowest writing energy consumed in our device is 0.6 nJ, and can be further reduced by reducing the channel dimension and the pulse width. The variation of magnetic transport properties of the device with gating is also studied. Along with the emulation of synaptic functions, the device showed dynamic filtering behavior and can act as a high-pass filter. These results provide an insight into the potential application of ferromagnetic metal-based synaptic transistor for large-scale, low power consuming, silicon compatible neuromorphic computing network.

**References:**

1 J.T. Yang, C. Ge, J.Y. Du, H.Y. Huang, M. He, C. Wang, H. Bin Lu, G.Z. Yang, and K.J. Jin, Adv. Mater. 30, 1 (2018). 2 W. Xu, S.Y. Min, H. Hwang, and T.W. Lee, Sci. Adv. 2, 1 (2016). 3 Y. Van De Burgt, E. Lubberman, E.J. Fuller, S.T. Keene, G.C. Faria, S. Agarwal, M.J. Marinella, A. Alec Talin, and A. Salleo, Nat. Mater. 16, 414 (2017). 4 C.J. Wan, Y.H. Liu, P. Feng, W. Wang, L.Q. Zhu, Z.P. Liu, Y. Shi, and Q. Wan, Adv. Mater. 28, 5878 (2016).

**Images:**

![](https://assets.underline.io/uploads/markdown_image/1/image/4dab6706c8eb7a634a11f93f16285802.jpg)
Fig. 1(a) Transfer curve measured at a different sweeping rate, ISD= 10 µA, channel conductance shown as a function of gate voltage. (b) The conductance modulation with the sweeping rate. (c) Multilevel, nonvolatile, reversible conducting states realized by applying 30 s gate pulses of different amplitude and brought back to the initial state by applying a negative pulse.

![](https://assets.underline.io/uploads/markdown_image/1/image/869a416cb7a3e2e10b9634698752e378.jpg)
Fig. 2 (a) Excitatory postsynaptic conductance (EPSC) triggered by positive presynaptic spike, (2 V, 20 s), ISD= 10 µA (b) Paired pulse facilitation (PPF) by applying pair of pulses (2 V, 5 s), 3s apart (c) EPSC triggered by presynaptic spikes of different amplitude (Δt= 30 s) showing the transition from STM to LTM (d) Long term potentiation (LTP) triggered by 50 consecutive presynaptic spikes (2 V, 3 s) (d) Biological potentiation and depression in series with 20 positive and negative pulses, VG = ± 1 V (e) Cycles of potentiation and depression showing repeatability of emulation of synaptic function.

 Based Synaptic Transistor for Neuromorphic Computing

Neuromorphic computing is attracting the attention of researchers for its usefulness in ultra-low-power computing applications1. Among the several magnetic and non-magnetic candidates to achieve low-power neuron and synaptic devices, the domain wall magnetic tunnel junctions (DW-MTJ) are the most promising contenders for energy efficiency. However, to realize the synaptic functions in neuromorphic devices, one must achieve multi-resistance states. The pinning sites can aid in achieving the required multi-resistance states2. Therefore, we propose meander DW devices, where segments of DW-MTJ wires join at an offset, which alters the energy landscape locally and helps in achieving deterministic DW motion. To study the proposed concept, we optimized the DW-MTJ stack with the perpendicular magnetic anisotropy to achieve a higher tunnel magnetoresistance (TMR) of 169% and a high RA (resistance-area product) of 241 Wmm2 suitable for DW devices with a large area. Subsequently, we measured the magnetic hysteresis (M-H) loops of the thin films to observe the layer-wise switching along with magnetic field variation (fig. 1 (a)). We have also measured the minor loop (fig. 1 (b)) and found that the coercivity of the free layer is ~21 Oe, which is suitable for energy-efficient DW motion. Next, we optimized the fabrication process for 3-terminal synaptic devices (fig. 1 (c)). We fabricated a sequence of devices with a DW-MTJ wire width w = 10 mm and corresponding offset (d) values ranging from 40% (of w) to 70% in the steps of 10%. Further, we fabricated three types of devices for every offset; (i) constant offset, increase in offset by (ii) 5%, and (iii) 10% over successive pinning sites. Finally, we performed field and current-driven DW motion experiments and witnessed the synaptic functions. Here we present the results of field-driven DW motion in fig. 1 (d). The stack details, process insights, and experimental findings will be presented in detail during the conference.

![](https://assets.underline.io/uploads/markdown_image/1/image/5676d1e7a6f58421533aa347a7748345.jpg)

Figure. (a) M-H loop of the full stack, (b) minor loop illustrating the switching of the free layer, (d) micrograph of the 3-terminal synaptic device, and (e) read-out TMR signal of field driven-DW motion.

Terminal Domain Wall Device Based Synapses for Neuromorphic Computing

Spin-based neuromorphic computing (NC) is investigated as a power-efficient enabler for artificial intelligence [1-3]. Hence, magnetic tunnel junction (MTJ) based domain wall devices are investigated as synthetic synapses and neurons to mimic their biological counterparts [2-3]. While a biological neuron resets itself after firing, such a function has not been demonstrated experimentally in spintronic systems [3]. The self-reset feature of neurons reduces the power consumption and the complexity of NC architecture. In this work, we present a domain wall (DW) based artificial neuron that automatically resets without applying a reset pulse. At first, we simulated a synthetic antiferromagnetic (SAF) DW device where the free layer (CoFeB) is driven by spin-transfer-torque (STT) to achieve integrate function. Leakage function was achieved when the antiferromagnetic coupling (AFC) between the free and pinned layers pushes the DW towards the starting position automatically (Fig. 1). The left quarter region of the free layer is ferromagnetically coupled (FC) to the pinned layer so that as DW returns (during reset), it is stabilized at the initial position (Fig. 2(a) and Fig. 2(b)). Experimentally, the integrate and leakage functions are also achieved as illustrated in Fig. 2(d) and Fig. 2(e), suggesting that an SAF-based neuron is viable for NC.

![](https://assets.underline.io/uploads/markdown_image/1/image/4f911e30d06059db93a1e69c895660a8.jpg)

Fig.1 Proposed design of the artificial neuron. Domain wall is moved forward by STT and (after firing), reset backward by SAF coupling.


![](https://assets.underline.io/uploads/markdown_image/1/image/c297c876d46646d24593590c9f897b4d.jpg)

Fig. 2 (a and b) DW position (free layer) vs time graphs for different AFC strength in the simulations. (c, d and e) Kerr images of the fabricated SAF neuron device with magnetic field in the -z direction. (c) No current applied and device is in antiparallel configuration, (d) current is applied, pushing the DW and achieving the integrate function, and (e) current is removed and the device automatically resets, indicating the leakage function.

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Presentations

Domain wall pinning through nanoscale interfacial Dzyaloshinskii-Moriya interaction

Synthetic antiferromagnetic domain wall device as artificial neuron for neuromorphic computing

Domain wall device with a graded anisotropy field as artificial neuron for neuromorphic computing

Based Synaptic Transistor for Neuromorphic Computing

Terminal Domain Wall Device Based Synapses for Neuromorphic Computing

Synthetic antiferromagnetic domain wall device as artificial neuron for neuromorphic computing

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