

The second-generation of Artificial intelligence (AI) has experienced intense growth due to its excellence in big-data applications. However, state-of-the-art AI lacks the advanced cognitive abilities seen in the brain, including the abilities to adapt to the environment, to understand context and to build coherent representations of the outside world. In addition, the implementation of second-generation AI with conventional CMOS uses large amounts of energy and area [1]. Spintronic nanodevices are particularly attractive for neuromorphic implementation of AI due to their small footprint, high endurance and low power consumption [2]. While these devices can implement ultra-low-power AI [3], they have yet to mimic the more rich, adaptive and computationally powerful behavior seen in the brain which forms the basis for advanced cognition. Here, we show the proof-of concept of a self-adaptive artificial neuron that utilizes adaptive spintronic materials that alter their structure and properties in response to external stimuli, in order to realize advanced cognitive abilities such as context-awareness and feature binding. The self-adaptive neuron is constructed from an artificial skyrmion lattice with five skyrmions hosted in a bilayer of thulium iron garnet (TmIG) and platinum (Pt). We report micromagnetic simulation results that show the neuron, when excited by an oscillating magnetic field, produces spin waves originating from skyrmion oscillations with a multi-frequency spectrum. The spectrum consists of four distinct resonant modes, identified as gyration, breathing, and hybridizations of both. Since each resonant frequency can represent one bit of information, the multi-frequent spectrum enables a large basis of information representation. Crucially, we show that both the amplitude and frequency of these resonant modes can be modulated by re-arranging the skyrmions in the lattice. This ability of the lattice to regulate its oscillatory dynamics in response to external input mimics a critical neural property called neuromodulation which plays a key role in advanced cognitive processes, including information transfer, decision-making, memory, object representation, consciousness etc. As a result of neuromodulation, the self-adaptive neuron demonstrates bio-plausible properties such as bursting and cross-frequency coupling [7], as well as advanced cognitive processes, namely context-awareness and feature binding. Context-awareness allows the neuron to make complex, high-level decisions while accounting for multiple factors to develop a deeper understanding of its circumstance. To implement context-awareness, the neuron receives a human spoken command and contextual information about the color of a box. Integrating both inputs, the neuron decides to open the box if and only if the human being gives the command to open and the box is not red in color. In addition, feature binding allows the neuron to correctly combine different segments of information (say, ‘red’ color and ‘circular’ shape) and construct a coherent percept (‘red circle’). To implement feature binding, a network of neurons receives visual information about two objects and processes information about their color and shape separately. The network then correctly binds together the two features (color and shape) to obtain coherent percepts of the original object. These results realize context-aware AI and the fusion of information from different sensory streams, which can have significant impact on human-machine collaboration, bio-medicine, smart energy, advanced manufacturing, agriculture and education. Adaptive materials can thus help realize neuromorphic circuits with advanced cognitive abilities for third generation AI, one that understands and adapts to a complex and ever-changing environment, learns without supervision from small datasets and better collaborates with human beings.

**References:**

[1] Cisco, Cisco Global Cloud Index: Forecast and methodology 2016–2021,  1513879861264127, (2018). [2] S. Singh, A. Sarma, N. Jao et al., ACM/IEEE 47th Annual International Symposium on Computer Architecture (ISCA), Valencia, Spain (2020). [3] P. A. Merolla, et al., Science, vol. 345, pp. 668-673 (2014). [4] E. Marder, Advances in Neural Information Processing Systems 6 (NIPS) (1993). [5]         L. Gao, P.-Y. Chen and S. Yu, Appl. Phys. Lett., vol. 111, p. 103503, (2017). [6] T. Jackson, S. Pagliarini and L. Pileggi, 28 nm CMOS, IEEE International Conference on Rebooting Computing (2018). [7]      R. F. Helfrich and R. T. Knight, Trends in Cognitive Sciences, vol. 20, p. 916, (2016).

**Images:**





 Advanced cognitive computing using adaptive spintronic materials INVITED

Magnetic tunnel junctions (MTJs) show great promise for implementation in high-performance spin transfer torque magnetic random-access memory (STT-MRAM) [1] and novel computing regimes such as magnetic domain wall logic [2] and neuromorphic computing [3]. MTJs consist of an insulating thin film tunneling barrier sandwiched between two ferromagnet electrodes. By altering the magnetization of the electrodes, the rate of tunneling across the barrier can be varied. This provides a mechanism to convert nonvolatile magnetic information to electrical information via modifying the resistivity of the device, achieving on/off tunnel magnetoresistance ratios (TMR) of over 600% at room temperature in state-of-the-art CoFeB/MgO/CoFeB MTJs [4]. Unfortunately, a handful of material setbacks stand in the way of the widespread adoption of even leading MgO MTJs over other emerging technologies, such as Resistive-RAM junctions, in next-generation architectures. MgO’s wide bandgap (7.8 eV) is associated with a high junction resistance-area (RA) product, greatly increasing power-draw and necessitating high operating voltages, which leads to device variability challenges and dielectric breakdown [5]. Additionally, MgO creates magnetic dead layers through the oxidation of interfacial Fe atoms and requires ultra-thin barrier layers that are difficult to fabricate and may contain pinholes [6]. These setbacks result in a reduction of the effective TMR and an increase in the RA-product, which motivates the search for new barrier materials. Here, we explore the properties of rock-salt structured Scandium Nitride (ScN) magnetoresistive junctions and find it a promising barrier material given its novel electron symmetry filtering properties, high TMR, and low RA-product. Fe/ScN/Fe MTJ supercells were constructed for 3,5,6,7 and 8 atomic layers of ScN, with the ScN lattice rotated by 45 degrees such that the N anions rest over the interfacial Fe, analogous to the Fe/MgO interface. Using the plane-wave basis density functional theory (DFT) package VASP [9], the MTJ supercells were converged and exported to Quantum Espresso [10] to calculate the complex bands, symmetry properties, and to perform spin-polarized electron transport calculations under the Landauer-Büttiker formalism as implemented in PWCOND [9]. A Hubbard +U correction of 4.5 eV was applied to Sc’s 3d orbital to correct the electronic band gaps using the PBE-GGA pseudopotentials. In this manner, the indirect Γ-X gap of the ScN was matched near to experimental values [7] at 1.31 eV. The conductance calculations find TMR ratios exceeding 1900% for sufficiently thick systems (&gt; 6 atomic layers of ScN), with spin-dependent transport dominated by unique Δ2’ band filtering through Sc’s 3d orbital in the 6-layer system analysed [12]. This mechanism is different from the spin filtering in Fe/MgO/Fe MTJs, where MgO selects for the half-metallic Δ1 band present in bcc Fe or CoFe electrodes [8]. Instead, ScN selects for Fe’s Δ2’ electrons with large crystal momentum, which is only provided in the majority spin of the Fe band structure at the fermi energy. Even with the high TMR and symmetry filtering in ScN, the layer-dependent calculations predict that the devices remain highly conductive with respect to barrier thickness, unlike in MgO systems where the dominant Δ1 electron wavefunctions decay rapidly across the barrier [8]. Fabricated in a system with minimal symmetry mixing and atomic dislocations, magnetoresistive ScN junctions could operate with a fraction of the power of MgO MTJs, while allowing for thicker barriers with more lenient manufacturing tolerances and maintaining high TMRs characteristic of MgO MTJs. Partial funding from Sandia National Lab (SNL). SNL is managed and operated by NTESS under DOE NNSA contract DE-NA0003525

**References:**

[1] H. Noguchi, K. Ikegami, S. Takaya, ISSCC 132, (2016) [2] J. A. Currivan-Incorvia, S. Siddiqui, S. Dutta, Nat. Comms. 7, 3 (2016). [3] N. Hassan, X. Hu, L. Jiang-Wei, J. Appl. Phys. 124, (2018). [4] S. Ikeda, J. Hayakawa, Appl. Phys. Lett. 93, 1 (2008) [5] L. Xue, A. Kontos, IEEE Trans. Magn. 51, 1 (2015). [6] W. S. Zhao, Y. Zhang, T. Devolder, Microelectron. Reliab. 52, 1848 (2012). [7] D. Gall, M. Städele, Phys. Rev. B - Condens. Matter Mater. Phys. 63, 1251191 (2001). [8] W. H. Butler, STAM, 9(1), 014106. (2008) [9] G. Kresse and J. Furthmüller, Phys. Rev. B, 54:11169, (1996) [10] Giannozzi, P., Baroni, S., Bonini, N. Journal of Physics: Condensed Matter, 21(39), 395502 (2009) [11] A. Smogunov, A. Dal Corso, E. Tosatti, Phys. Rev. B 70, 045417 (2004) [12] S. Karki, V. Rogers, (under review) arXiv 2008.12770 (2020)

**Images:**

![](https://assets.underline.io/uploads/markdown_image/1/image/2eacbc5c29f6d3c4ffb033cdca39b0e2.jpg)
Fig. 1 Scattering states of parallel-magnetized Fe/6ScN/Fe majority spin (a) and minority spin (b) sampled at a high-transmission spike at kx = 0.02 (2π/a) and ky = 0.05 (2π/a).

![](https://assets.underline.io/uploads/markdown_image/1/image/9627d3f44227554116317204d460b021.jpg)
Fig. 2 Complex bands of Scandium Nitride sampled at the Γ and X point, with the symmetry-resolved real bands set in between. A small Hubbard +U bandgap correction is applied.

 Large magnetoresistance in symmetry-filtering scandium nitride junctions using first principles

Magnetic skyrmions are compact spin textures that show remarkably unique, particle-like properties. They are widely regarded as promising candidates for emergent magnetic devices as they are small in size and easily maneuverable [1]. The Dzyaloshinskii-Moriya interaction (DMI) is often critical to the generation and manipulation of skyrmions. However, there has been a fundamental lack of understanding of the microscopic origin of DMI or the mechanisms by which DMI stabilizes skyrmions in magnetic bilayers. Little has been known of the material parameters that determine the value of DMI. This knowledge is critical for rational design of skyrmion-hosting materials and further development of skyrmion technology [2]. Here, we address this knowledge gap and present a model to explain the microscopic origin of DMI in heavy metal/ferromagnet (HM/FM) bilayers. We calculate the DMI values in a series of HM/FM bilayers and explain the trend of DMI values observed [3]. We present a new theoretical model that explains the microscopic origin of DMI in these bilayers. We show that the value of DMI depends on two parameters, namely, interfacial hybridization and orbital contributions of the heavy metal. Using these parameters, we explain the experimentally observed DMI trends. Additionally, we report four new materials systems predicted to demonstrate giant DMI. In HM/FM bilayers, the size and stability of DMI-stabilized-skyrmions is decided by the interplay between the DMI, the exchange constant, the out-of-plane anisotropy and dipolar fields. Due to their giant DMI, our newly discovered materials are promising for hosting small sized skyrmions at room-temperature, enabling ultra-dense, ultra-low power skyrmion devices. Together, our results put forth a new understanding of DMI, discover promising materials and suggest pathways for the controlled generation of skyrmions.References: 1. A. Fert, V. Cros, and J. Sampaio, Nature nanotechnology, Vol. 8, p. 152 (2013) 2. W. Jiang et al., Physics Reports, Vol. 704, p. 1-49 (2017). 3. P. Jadaun, L. F. Register and S. K. Banerjee, npj Computational Materials, Vol. 6, p.88 (2020)

Design of giant DMI and its microscopic origin in magnetic bilayers

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Priyamvada Jadaun

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Advanced cognitive computing using adaptive spintronic materials INVITED

Large magnetoresistance in symmetry-filtering scandium nitride junctions using first principles

Design of giant DMI and its microscopic origin in magnetic bilayers

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