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1. Magnonic combinatorial memory

   https://doi.org/10.1038/s44306-023-00005-0  

   Balinskyy, Mykhaylo ; Khitun, Alexander ( February 2024 , npj Spintronics)

   In this work, we consider a type of magnetic memory where information is encoded into the mutual arrangement of magnets. The device is an active ring circuit comprising magnetic and electric parts connected in series. The electric part includes a broadband amplifier, phase shifters, and attenuators. The magnetic part is a mesh of magnonic waveguides with magnets placed on the waveguide junctions. There are amplitude and phase conditions for auto-oscillations to occur in the active ring circuit. The frequency(s) of the auto-oscillation and spin wave propagation path(s) in the magnetic part depends on the mutual arrangement of magnets in the mesh. The propagation path is detected with a set of power sensors. The correlation between circuit parameters and spin wave path is the basis of memory operation. The combination of input/output switches connecting electric and magnetic parts and electric phase shifters constitute the memory address. The output of the power sensors is the memory state. We present experimental data on the proof-of-the-concept experiments on the prototype with three magnets placed on top of a single-crystal yttrium iron garnet Y₃Fe₂(FeO₄)₃(YIG) film. There are three selected places for the magnets to be placed. There is a variety of spin wave propagation paths for each configuration of magnets. The results demonstrate a robust operation with an On/Off ratio for path detection exceeding 35 dB at room temperature. The number of possible magnet arrangements scales factorially with the size of the magnetic part. The number of possible paths per one configuration scales factorial as well. It makes it possible to drastically increase the data storage density compared to conventional memory devices. Magnonic combinatorial memory with an array of 100 × 100 magnets can store all information generated by humankind. Physical limits and constraints are also discussed.

    

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2. Hardware implementation of Bayesian network building blocks with stochastic spintronic devices

   https://doi.org/10.1038/s41598-020-72842-6  

   Debashis, Punyashloka ; Ostwal, Vaibhav ; Faria, Rafatul ; Datta, Supriyo ; Appenzeller, Joerg ; Chen, Zhihong ( September 2020 , Scientific Reports)

   Bayesian networks are powerful statistical models to understand causal relationships in real-world probabilistic problems such as diagnosis, forecasting, computer vision, etc. For systems that involve complex causal dependencies among many variables, the complexity of the associated Bayesian networks become computationally intractable. As a result, direct hardware implementation of these networks is one promising approach to reducing power consumption and execution time. However, the few hardware implementations of Bayesian networks presented in literature rely on deterministic CMOS devices that are not efficient in representing the stochastic variables in a Bayesian network that encode the probability of occurrence of the associated event. This work presents an experimental demonstration of a Bayesian network building block implemented with inherently stochastic spintronic devices based on the natural physics of nanomagnets. These devices are based on nanomagnets with perpendicular magnetic anisotropy, initialized to their hard axes by the spin orbit torque from a heavy metal under-layer utilizing the giant spin Hall effect, enabling stochastic behavior. We construct an electrically interconnected network of two stochastic devices and manipulate the correlations between their states by changing connection weights and biases. By mapping given conditional probability tables to the circuit hardware, we demonstrate that any two node Bayesian networks can be implemented by our stochastic network. We then present the stochastic simulation of an example case of a four node Bayesian network using our proposed device, with parameters taken from the experiment. We view this work as a first step towards the large scale hardware implementation of Bayesian networks.

    

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3. High-harmonic generation in spin and charge current pumping at ferromagnetic or antiferromagnetic resonance in the presence of spin–orbit coupling

   https://doi.org/10.1088/2515-7639/aceaad  

   Varela-Manjarres, Jalil ; Nikolić, Branislav K. ( August 2023 , Journal of Physics: Materials)

   One of the cornerstone effects in spintronics is spin pumping by dynamical magnetization that is steadily precessing (around, for example, thez-axis) with frequencyω₀due to absorption of low-power microwaves of frequencyω₀under the resonance conditions and in the absence of any applied bias voltage. The two-decades-old ‘standard model’ of this effect, based on the scattering theory of adiabatic quantum pumping, predicts that component$I^{S_z}$of spin current vector$(I^{S_x}(t),I^{S_y}(t),I^{S_z})\propto {\omega }_0$is time-independent while$I^{S_x}(t)$and$I^{S_y}(t)$oscillate harmonically in time with a single frequencyω₀whereas pumped charge current is zero$I\equiv 0$in the same adiabatic$\propto {\omega }_0$limit. Here we employ more general approaches than the ‘standard model’, namely the time-dependent nonequilibrium Green’s function (NEGF) and the Floquet NEGF, to predict unforeseen features of spin pumping: namely precessing localized magnetic moments within a ferromagnetic metal (FM) or antiferromagnetic metal (AFM), whose conduction electrons are exposed to spin–orbit coupling (SOC) of either intrinsic or proximity origin, will pump both spin$I^{S_{\alpha }}(t)$and chargeI(t) currents. All four of these functions harmonically oscillate in time at both even and odd integer multiples$N{\omega }_0$of the driving frequencyω₀. The cutoff order of such high harmonics increases with SOC strength, reaching$N_{\mathrm{m}\mathrm{a}\mathrm{x}}\simeq 11$in the one-dimensional FM or AFM models chosen for demonstration. A higher cutoff$N_{\mathrm{m}\mathrm{a}\mathrm{x}}\simeq 25$can be achieved in realistic two-dimensional (2D) FM models defined on a honeycomb lattice, and we provide a prescription of how to realize them using 2D magnets and their heterostructures.

    

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4. Field-induced quantum spin disordered state in spin-1/2 honeycomb magnet Na2Co2TeO6

   https://doi.org/10.1038/s41467-021-25567-7  

   Lin, Gaoting ; Jeong, Jaehong ; Kim, Chaebin ; Wang, Yao ; Huang, Qing ; Masuda, Takatsugu ; Asai, Shinichiro ; Itoh, Shinichi ; Günther, Gerrit ; Russina, Margarita ; et al ( December 2021 , Nature Communications)

   Abstract Spin-orbit coupled honeycomb magnets with the Kitaev interaction have received a lot of attention due to their potential of hosting exotic quantum states including quantum spin liquids. Thus far, the most studied Kitaev systems are 4 d /5 d -based honeycomb magnets. Recent theoretical studies predicted that 3 d -based honeycomb magnets, including Na 2 Co 2 TeO 6 (NCTO), could also be a potential Kitaev system. Here, we have used a combination of heat capacity, magnetization, electron spin resonance measurements alongside inelastic neutron scattering (INS) to study NCTO’s quantum magnetism, and we have found a field-induced spin disordered state in an applied magnetic field range of 7.5 T <  B (⊥ b -axis) < 10.5 T. The INS spectra were also simulated to tentatively extract the exchange interactions. As a 3 d -magnet with a field-induced disordered state on an effective spin-1/2 honeycomb lattice, NCTO expands the Kitaev model to 3 d compounds, promoting further interests on the spin-orbital effect in quantum magnets. 

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5. Low-Energy Deep Belief Networks using Intrinsic Sigmoidal Spintronic-based Probabilistic Neurons

   Zand, R. ; Camsari, K. Y. ; Pyle, S. D. ; Ahmed, I. ; Kim, C. H. ( May 2018 , ACM Great Lakes Symposium on VLSI)

   A low-energy hardware implementation of deep belief network (DBN) architecture is developed using near-zero energy barrier probabilistic spin logic devices (p-bits), which are modeled to real- ize an intrinsic sigmoidal activation function. A CMOS/spin based weighted array structure is designed to implement a restricted Boltzmann machine (RBM). Device-level simulations based on precise physics relations are used to validate the sigmoidal relation between the output probability of a p-bit and its input currents. Characteristics of the resistive networks and p-bits are modeled in SPICE to perform a circuit-level simulation investigating the performance, area, and power consumption tradeoffs of the weighted array. In the application-level simulation, a DBN is implemented in MATLAB for digit recognition using the extracted device and circuit behavioral models. The MNIST data set is used to assess the accuracy of the DBN using 5,000 training images for five distinct network topologies. The results indicate that a baseline error rate of 36.8% for a 784x10 DBN trained by 100 samples can be reduced to only 3.7% using a 784x800x800x10 DBN trained by 5,000 input samples. Finally, Power dissipation and accuracy tradeoffs for probabilistic computing mechanisms using resistive devices are identified. 

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