Emulation of neurobiological information processing by artificial neural networks enables new frontiers in efficiency when performing data-intensive and real-time artificial intelligence tasks1,2. In this work, we experimentally demonstrate a multi-weight (MW) artificial synapse using a domain wall-magnetic tunnel junction (DW-MTJ) with a straight, notched geometry, depicted in Fig. 1(a). While previous simulations show that DW-MTJ synapses have the necessary characteristics for neuromorphic computing, experimental work has focused on binary DW-MTJs3, and no experimental works have closely studied the behavior of the MW-DW-MTJ synapse, in contrast with works studying multi-MTJ devices4. The heavy metal/ferromagnet/oxide trilayer stack3 is patterned into a DW track with a single, long output MTJ centered on top of the track. To achieve MW synaptic switching, five notches are fabricated on the track (n1-5). Fig. 1(b) shows the MTJ resistance (RMTJ) vs. out-of-plane magnetic field for a synapse with DW track width w = 350 nm, showing the maximum and minimum RMTJ. To study synaptic switching behaviors, voltage V is applied between the IN and CLK terminals (50 ns pulses of increasing amplitude, with step size 0.1 V) to move the DW from notch to notch via current driven via spin orbit torque. Fig. 2(a) shows RMTJ vs. V for 10 cycles, showing five resistances each corresponding to the DW in one of the notches. From the spacing dimensions of the notches, we estimate RMTJ when the DW is pinned at each notch, as shown in Fig. 2(b), which are close to the resistances observed in Fig. 2(a). We will show that repeated single-amplitude V pulses also move the DW notch to notch but ramping up the amplitude allows us to observe all switches without tuning the amplitude precisely. We will show how the cycle-to-cycle stochasticity may aid certain online learning tasks when V can be applied repeatably over time to set the synapse weight, allowing for stochastic outer product updates. This work informs essential design parameters using DW-based synapses for future neuromorphic computing.

![](https://assets.underline.io/uploads/markdown_image/1/image/0326a4371e8db824bf485edae008119b.jpg)

Fig. 1. (a) DW-MTJ synapse. (b) Field loop.

![](https://assets.underline.io/uploads/markdown_image/1/image/25feb81acfb52d72151ddba93b51f603.jpg)

Fig. 2. (a) MW switching for 10 cycles. (b) Expected vs measured RMTJ when the DW is pinned at each notch.

Multi-Weight Artificial Synapses with Straight Notched Geometry for Neuromorphic Computing

The development of efficient processing of unstructured information necessitates research into neuromorphic computing. In this work, we report domain wall-magnetic tunnel junction (DW-MTJ) artificial synaptic devices with a trapezoidal geometry that are uniquely suited for neuromorphic computation. DW-MTJ devices are nonvolatile memory-in-logic elements that can be integrated into an artificial neural network1. The bottom ferromagnetic layer of the MTJ is a DW track, and the magnetization of that layer is determined by the position of the DW within the track. The device can act as a programmable resistor based on the location of the DW. Simulations have shown that this high level of resistance state control and stability could allow for advanced functionalities for neuromorphic computing1,2,3. However, experimental work has focused on binary DW-MTJs4, and almost no experimental work has been done to understand the behavior of the multi-weight (MW) DW-MTJ synapse, with one paper showing results on a multi-MTJ device5. Here, we present a DW-MTJ based MW synapse that is directional, has stable and repeatable resistance states, and uses a single MTJ. The MTJ was lengthened and pinning notches were fabricated along the DW track. This way the DW could rest under the MTJ, opening more resistance states. A trapezoidal DW track was patterned for directional switching, shown in Fig. 1. As the DW is moved from IN to CLK using spin orbit torque switching, the voltage amplitude required to propagate the DW increases as the track widens, which results in MW switching. Going CLK to IN does not exhibit MW switching since depinning from the right-most notch requires the maximum voltage, shown in Fig. 2. We will show how this asymmetry in writing and reset is useful for offline learning to bring the synapse to a resistance state using a particular voltage, and then to reset the device efficiently. We will compare the behavior of the trapezoidal DW-MTJ synapse to the straight DW-MTJ synapse presented separately.

![](https://assets.underline.io/uploads/markdown_image/1/image/d8758b10df5900a61b218c67399668ce.jpg)

Fig. 1: Trapezoidal Synapse. Left) SEM image. Right) Schematic with DW track in blue, MTJ in red, electrodes in green.

![](https://assets.underline.io/uploads/markdown_image/1/image/00ff215da248f99f97ffb70a113e6586.jpg)

Fig. 2: Three consecutive directional write then reset cycles

Multi-Weight Directional Artificial Synapses with Trapezoidal Geometry for Neuromorphic Computing

There are many potential applications for spintronics including in-memory, neuromorphic, and radiation hard computing. In- memory computing offers a solution to the memory wall bottleneck that hinders traditional architectures. Neuromorphic computing has the potential to revolutionize how data-intensive tasks, such as image recognition, are computed by emulating how a biological brain operates. Radiation hard computing is useful for certain situations, mainly space applications, where the Earth’s atmosphere does not protect sensitive CMOS hardware. All of these applications can be achieved using domain wall motion-based devices. The domain wall magnetic tunnel junction (DW-MTJ) device architecture is explored in this work as shown in figure 1. DW-MTJ devices operate by sending a current pulse along a ferromagnetic wire to propagate a domain wall from the input to the output of the device. As the domain wall passes under the MTJ, the magnetization state of the bottom layer of the CoFeB/MgO/CoFeB junction changes. By controlling where the domain wall is, the MTJ can have programmable resistance. We use current driven domain wall motion to electrically switch the devices. It has been shown that spin logic requires high tunneling magnetoresistance, low switching voltages, and high stability. These hurdles have been overcome in this work by utilizing perpendicular anisotropy, spin orbit torque switching, and optimized fabrication. Incorvia et al. in 2015 demonstrated that information could be encoded in a magnetic wire by moving a domain wall, but the TMR of those devices was 12% [1]. Here, we develop a process that maintains as much TMR as possible, achieving TMR &gt; 150%, as well as no reduction in resistance-area (RA) product after device fabrication. There were several improvements made to the previous fabrication process. The main change was to remove the reactive ion etch step to contact the MTJ. Incorvia et al. opened a via in polymethylmethacrylate (PMMA) and then used a CF4 reactive ion etch through a hydrogen silsesquioxane (HSQ) layer to expose the MTJ and wire contacts. In our new process, we replaced this step with a negative tone resist and encapsulation step to achieve the same result of an open via to the MTJ and domain wall track without exposing the sensitive layers to a reactive etch. This way the MTJ is never directly exposed: during all etches and encapsulation, the MgO is protected by negative resist. Furthermore, the encapsulation was performed at relatively low temperatures of 100 °C to prevent any additional loss of TMR, and silicon nitride was used instead of silicon oxide to reduce oxidation. These factors allowed our devices to maintain their high TMR post-processing. The TMR is shown in the field loops of four devices in figure 2. The average achieved was 164±17%, compared to the unpatterned film TMR of 168±6%. Before fabrication, the film had RA = 35±2 Ω-μm2 and the devices have RA = 31±3 Ω-µm2. This indicates that our fabrication process had little to no negative impact on the magnetic integrity of our material. These devices utilize electrical initialization of domain walls rather than an external magnetic field, so they can be fabricated as straight wires rather than curved. This allows for increased density if this technology becomes industrialized. Previous iterations of these devices were curved to allow an external magnetic field to nucleate a domain wall at an initial position. Here, we opted for an electrical approach by implementing an oersted field line near the left contact of each device. A short pulse is sent through this line which nucleates a domain wall in the ferromagnetic track [2]. This is one step closer towards all electric operation of the memory-in-logic devices. These oersted field lines increased stability and repeatability by nucleating the domain wall in the same position each time, rather than an unpredictable location via external magnetic field nucleation. Calculating the variability based on the switching voltage range over 10 cycles with both nucleation methods showed an improvement from external magnetic field (105%) variability to the oersted field line (7%) variability. We show in simulation that these improvements bring the variability of the technology within the regime for large circuit function such as a 32-bit full adder [3]. Partial funding from Sandia National Lab (SNL). SNL is managed and operated by NTESS under DOE NNSA contract DE-NA0003525.

**References:**

1J. A. Currivan-Incorvia, S. Siddiqui, S. Dutta, E. R. Evarts, C. A. Ross & M. A. Baldo. 2015 IEEE Int. Electron Devices Meet. 32–36 (2015) 2G. Zahnd, V. T. Pham, A. Marty, M. Jamet, C. Beigné, L. Notin, C. Vergnaud, F. Rortais, L. Vila & J. P. Attané. J. Magn. Magn. Mater. Vol. 406, p. 166–170 (2016) 3T. Patrick Xiao, M. J. Marinella, C. H. Bennett, X. Hu, B. Feinberg, R. Jacobs-Gedrim, S. Agarwal, J. S. Brunhaver, J. S. Friedman & J. A. C. Incorvia. IEEE J. Explor. Solid-State Comput. Devices Circuits., Vol. 5, p. 188-196 (2019)

**Images:**

![](https://assets.underline.io/uploads/markdown_image/1/image/bee6a10b4751acb8eb32a297ccf3365a.jpg)
Fig.1: Top-down SEM image of a DW-MTJ device. Patterned domain wall track (labeled DW track) and magnetic tunnel junction (labeled MTJ) identified. Device width (labeled w) is 450 nm. Widths down to 250 nm were patterned and tested.

![](https://assets.underline.io/uploads/markdown_image/1/image/694813823627de4f60ea1974a8edb598.jpg)
Fig. 2: Field loop for devices in this work. RMTJ is the measured resistance through the tunnel junction. Spin-orbit torque current switching was also performed and will be presented.

 Domain Wall-Magnetic Tunnel Junction Spin Orbit Torque Devices for In-Memory Computing

Progress has been made on emulating the neuron and the synapse using magnetic devices [1], [2] especially with three-terminal magnetic tunnel junctions (3T-MTJs) [3], [4]. Increasingly more accurate biological mimics are being developed with the 3T-MTJ like the Leaky-Integrate-Fire (LIF) [5] neuron displaying lateral inhibition, and the 3T-MTJ synapse model showing spike timing dependent plasticity (STDP) [6], [7]. However, more work must be done to achieve real-time machine learning. Using an earlier developed synapse circuit [8] and a 3T-MTJ spice model [9], we show the transient behavior of a 2 by 2 crossbar array (cartoon in Fig 1). There is STDP for different delay conditions between the pre- and post-synaptic neural spikes. The shorter the time difference between the onset of the pre- and post-synaptic neuron spike, the higher the current through the ferromagnet (Fig 2). Higher current leads to higher rate of change of the domain wall (DW) position per neural event and higher DW displacement. At t=0, the initial pair of presynaptic and postsynaptic pulses helps to randomly give the DWs a head start. DW for synapse S2 (red plot) rises faster because it has the least delay of 1 ns between its pre- and post-synaptic pulse despite its pulses lagging that of S1 whose DW had a head start. Scaling this up, the 3T MTJ synapse is robust in more complex systems such as a machine learning clustering task.References: [1] S. Lequeux et al., Sci. Rep., vol. 6, no. August, 2016, doi: 10.1038/srep31510. [2] J. Torrejon et al., Nature, vol. 547, no. 7664, pp. 428–431 (2017) [3] N. K. Upadhyay, H. Jiang, Z. Wang et al, Adv. Mater. Technol., vol. 1800589, pp. 1–13 (2019) [4] N. Hassan et al., J. Appl. Phys., vol. 124, no. 15, p. 152127 (2018) [5] G. Bi and M. Poo, Annu. Rev. Neurosci., vol. 24, no. 1, pp. 139–166 (2002) [6] H. Markram, W. Gerstner, and P. J. Sjöström, Front. Synaptic Neurosci., vol. 4, pp. 2–5 (2012) [7] O. Akinola, X. Hu, C. H. Bennett et al, J. Phys. D. Appl. Phys., vol. 52, no. 49 (2019) [8] X. Hu, A. Timm, W. H. Brigner et al, IEEE Trans. Elect. Dev., vol. 66, no. 6, pp. 2817–2821 (2019)

Online Training of Spiking Neural Networks Using Domain Wall Magnetic Tunnel Junction Synapses

**Session chair:** Stephane Mangin, Université de Lorraine


GOK - MRAM, Magnetic Logic and Related Devices I

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A5: Neuromorphic Computing with Domain Walls and Chiral Spin Textures

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Multi-Weight Artificial Synapses with Straight Notched Geometry for Neuromorphic Computing

Multi-Weight Directional Artificial Synapses with Trapezoidal Geometry for Neuromorphic Computing

Domain Wall-Magnetic Tunnel Junction Spin Orbit Torque Devices for In-Memory Computing

Online Training of Spiking Neural Networks Using Domain Wall Magnetic Tunnel Junction Synapses

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