arXiv subjects

Artem Litvinenko

Publications and source records attributed to Artem Litvinenko.

At least 19 recordsLinked to original sources

arXiv2026

Phase noise analysis and control of VO$_2$-based relaxation type oscillators

VO$_2$-based relaxation oscillators form a rapidly developing field that finds applications in neuromorphic computing, Ising machines, and numerous signal processing concepts. These oscillators operate in a deeply nonlinear relaxation regime based on rapid phase transitions between insulating and metallic states in the VO$_2$ material. This process is governed by thermal effects, which lead to additional voltage fluctuations and contribute to a considerably wide spectral linewidth in the VO$_2$-based oscillator signal. In this work, we thoroughly study the phase noise in VO$_2$-based relaxation oscillators and demonstrate that the broadening of the generation spectrum linewidth at low oscillation frequencies is caused by an increased susceptibility to thermal fluctuations during the incubation phase. We explore the types of noise affecting oscillator stability and show that synchronization with an external square-wave signal improves the phase noise more effectively than a sinusoidal-shape injection locking signal.

physics.app-ph↗

arXiv2026

A 2048-spin bulk acoustic wave Ising machine for number partitioning and Sudoku

Optical coherent Ising machines based on time-multiplexing have demonstrated significant progress in terms of connectivity and spin scalability. However, they are constrained by large physical footprints, high power consumption, poor thermal stability, and high cost. Here, we present a time-multiplexed Ising machine leveraging propagating wave packets in solid-state delay lines at microwave frequencies, enabling thermally stable, robust, low-power, tabletop, and affordable design. We use two serially connected 20.5 MHz, 707 microseconds bulk acoustic wave delay lines supporting 2,048 spins. Our design provides all-to-all connectivity with 15-bit coupling resolution and finds approximate MAX-CUT solutions in 341 ms, potentially scalable to sub-ms by using higher frequency delay lines. Additionally, we demonstrate solutions to number partitioning and Sudoku problems. Compared with state-of-the-art Coherent Ising machines, our machine exhibits four orders of magnitude higher thermal stability. Against the simulated bifurcation algorithm, our design achieves comparable results on the MAX-CUT problem, while outperforming it on the more complex number-partitioning and Sudoku problems.

cond-mat.mes-hall↗

arXiv2026

Biologically Realistic Dynamics for Nonlinear Classification in CMOS+X Neurons

Spiking neural networks encode information in spike timing and offer a pathway toward energy efficient artificial intelligence. However, a key challenge in spiking neural networks is realizing nonlinear and expressive computation in compact, energy-efficient hardware without relying on additional circuit complexity. In this work, we examine nonlinear computation in a CMOS+X spiking neuron implemented with a magnetic tunnel junction connected in series with an NMOS transistor. Circuit simulations of a multilayer network solving the XOR classification problem show that three intrinsic neuronal properties enable nonlinear behavior: threshold activation, response latency, and absolute refraction. Threshold activation determines which neurons participate in computation, response latency shifts spike timing, and absolute refraction suppresses subsequent spikes. These results show that magnetization dynamics of MTJ devices can support nonlinear computation in compact neuromorphic hardware.

cs.NE↗

arXiv2025

A CMOS+X Spiking Neuron With On-Chip Machine Learning

We present the design and numerical simulation of a spiking neuron in a proof-of-concept model of on-chip machine learning. Built within the CMOS+X framework, the spiking neuron consists of an NMOS transistor combined with a magnetic tunnel junction (MTJ). This NMOS+MTJ unit, when simulated in the industry-standard circuit simulation software ``LTspice'', reproduces multiple functions of a biological neuron, including threshold spiking, latency, refractory periods, synaptic integration, and inhibition. These behaviors arise from the intrinsic magnetization dynamics of the MTJ and do not require any additional control circuitry. By interconnecting the NMOS+MTJ neurons, we construct a model of an analog multilayer network that learns through spike-timing-dependent weight updates derived from a gradient-descent rule, with both training and inference modeled in the analog domain. The simulated network demonstrates spike propagation and successful training on the XOR task under the modeled conditions.

physics.app-ph↗

arXiv2025

Metrics for spin-based computing

Spin-based computing is emerging as a powerful approach for energy-efficient and high-performance solutions to future data processing hardware. Spintronic devices function by electrically manipulating the collective dynamics of the electron spin, that is inherently non-volatile, nonlinear and fast-operating, and can couple to other degrees of freedom such as photonic and phononic systems. This review explores key advances in integrating magnetic and spintronic elements into computational architectures, ranging from fundamental components like radio-frequency neurons/synapses and spintronic probabilistic-bits to broader frameworks such as reservoir computing and magnetic Ising machines. We discuss hardware-specific and task-dependent metrics to evaluate the computing performance of spin-based components and associate them with physical properties. Finally, we discuss challenges and future opportunities, highlighting the potential of spin-based computing in next-generation technologies.

cond-mat.mes-hall↗

arXiv2025

A Physics-Based Circuit Model for Magnetic Tunnel Junctions

This work presents an equivalent circuit model for Magnetic Tunnel Junctions (MTJs) that accurately captures their magnetization dynamics and electrical behavior. Implemented in LTspice, the model is validated against direct numerical solutions of the Landau-Lifshitz-Gilbert-Slonczewski (LLGS) equation. It effectively simulates essential spintronic phenomena, including ferromagnetic resonance, field- and spin-torque-induced switching, and spin-torque-induced oscillations. Simulation results demonstrate strong agreement between LTspice and LLGS solutions, confirming the model accuracy and utility for efficient circuit-level analysis of spintronic devices. The ability to incorporate time-dependent magnetic fields and voltage inputs makes the proposed model suitable for diverse applications such as neuromorphic computing, microwave signal processing, and spintronic memory technologies. By providing a computationally efficient yet physically accurate circuit representation, this work enables seamless integration of MTJs into larger electronic systems, potentially accelerating the development of advanced spintronic circuit architectures.

cond-mat.mes-hall↗

arXiv2025

Ultra-large mutually synchronized networks of 10 nm spin Hall nano-oscillators

While mutually interacting spin Hall nano-oscillators (SHNOs) hold great promise for wireless communication, neural networks, neuromorphic computing, and Ising machines, the highest number of synchronized SHNOs remains limited to $N$ = 64. Using ultra-narrow 10 and 20-nm nano-constrictions in W-Ta/CoFeB/MgO trilayers, we demonstrate mutually synchronized SHNO networks of up to $N$ = 105,000. The microwave power and quality factor scale as $N$ with new record values of 9 nW and $1.04 \times 10^6$, respectively. An unexpectedly strong array size dependence of the frequency-current tunability is explained by magnon exchange between nano-constrictions and magnon losses at the array edges, further corroborated by micromagnetic simulations and Brillouin light scattering microscopy. Our results represent a significant step towards viable SHNO network applications in wireless communication and unconventional computing.

cond-mat.mes-hall↗

arXiv2024

A frequency tunable low-noise YIG-GGG based oscillator with strong magneto-elastic coupling

We present a frequency tunable magneto-acoustic oscillator (MAO) operating in low-phase-noise and complex dynamical regimes based on a single composite YIG-GGG resonator. The magneto-acoustic resonator (MAR) is based on a YIG (yttrium iron garnet) layer epitaxially grown on a GGG (gadolinium gallium garnet) substrate. By optimizing the YIG thickness, we obtain a high magneto-elastic coupling of around 1 MHz between the ferromagnetic resonance (FMR) in YIG and high overtone acoustic resonances (HBARs) in the YIG-GGG structure in the 1-2 GHz frequency range. It allows to eliminate the need for pre-selectors and bulky circulators, thus simplifying the MAO design while maintaining the possibility to lock to HBAR YIG-GGG modes. With an adjustment in the loop over-amplification parameter, the MAO can be locked either only to high-Q magneto-acoustic HBARs or to both types of resonance including HBARs and the FMR mode of the YIG film. In a low-phase-noise regime, MAO generates only at certain values of the applied field and exhibits discrete frequency tunability with a 3.281 MHz step corresponding to the frequency separation between the adjacent HBAR modes in a YIG-GGG structure. In a complex regime where oscillation conditions expand to include both HBAR and FMR modes, MAO demonstrates continuous generation as the function of the applied field with variable phase noise parameters. Moreover, in low-phase-noise regime, MAO phase noise plot improves by 30 dB compared to the operational regime locked to the pure FMR in YIG which is in agreement with the measured FMR and HBAR Q-factors.

physics.app-ph↗

arXiv2024

Ultra-fast spin Hall nano-oscillator based microwave spectral analysis

Ultra-fast spectrum analysis concept based on rapidly tuned spintronic nano-oscillators has been under development for the last few years and has already demonstrated promising results. Here, we demonstrate an ultra-fast microwave spectrum analyzer based on a chain of five mutually synchronized nano-constriction spin Hall nano-oscillators (SHNOs). As mutual synchronization affords the chain a much improved signal quality, with linewidths well below 1 MHz at close to a 10 GHz operating frequency, we observe an order of magnitude better frequency resolution bandwidth compared to previously reported spectral analysis based on single magnetic tunnel junction based spin torque nano-oscillators. The high-frequency operation and ability to synchronize long SHNO chains and large arrays make SHNOs ideal candidates for ultra-fast microwave spectral analysis.

cond-mat.mes-hall↗

arXiv2024

A numerical model for time-multiplexed Ising machines based on delay-line oscillators

Ising machines (IM) have recently been proposed as unconventional hardware-based computation accelerators for solving NP-hard problems. In this work, we present a model for a time-multiplexed IM based on the nonlinear oscillations in a delay line-based resonator and numerically study the effects that the circuit parameters, specifically the compression gain $β_r$ and frequency nonlinearity $β_i$, have on the IM solutions. We find that the likelihood of reaching the global minimum -- the global minimum probability (GMP) -- is the highest for a certain range of $β_r$ and $β_i$ located near the edge of the synchronization region of the oscillators. The optimal range remains unchanged for all tested coupling topologies and network connections. We also observe a sharp transition line in the ($β_i, β_r$) space above which the GMP falls to zero. In all cases, small variations in the natural frequency of the oscillators do not modify the results, allowing us to extend this model to realistic systems.

math-ph↗

arXiv2024

Spin Hall Nano-Oscillator Empirical Electrical Model for Optimal On-chip Detector Design

As nascent nonlinear oscillators, nano-constriction spin Hall nano-oscillators (SHNOs) represent a promising potential for integration into more complicated systems such as neural networks, magnetic field sensors, and radio frequency (RF) signal classification, their tunable high-frequency operating regime, easy synchronization, and CMOS compatibility can streamline the process. To implement SHNOs in any of these networks, the electrical features of a single device are needed before designing the signal detection CMOS circuitry. This study centers on presenting an empirical electrical model of the SHNO based on a comprehensive characterization of the output impedance of a single SHNO, and its available output power in the range of 2-10 GHz at various bias currents.

cond-mat.mes-hall↗

arXiv2024

Spintronic devices as next-generation computation accelerators

The ever increasing demand for computational power combined with the predicted plateau for the miniaturization of existing silicon-based technologies has made the search for low power alternatives an industrial and scientifically engaging problem. In this work, we explore spintronics-based Ising machines as hardware computation accelerators. We start by presenting the physical platforms on which this emerging field is being developed, the different control schemes and the type of algorithms and problems on which these machines outperform conventional computers. We then benchmark these technologies and provide an outlook for future developments and use-cases that can help them get a running start for integration into the next generation of computing devices.

cond-mat.mes-hall↗

arXiv2023

Mutual synchronization in spin torque and spin Hall nano-oscillators

This chapter reviews the state of the art in mutually synchronized spin-torque and spin Hall nano-oscillator (STNO and SHNO) arrays. After briefly introducing the underlying physics, we discuss different nano-oscillator implementations and their functional properties with respect to frequency range, output power, phase noise, and modulation rates. We then introduce the concepts and the theory of mutual synchronization and discuss the possible coupling mechanisms in spintronic nano-oscillators, such as dipolar, electrical, and spin-wave coupling. We review the experimental literature on mutually synchronized STNOs and SHNOs in one- and two-dimensional arrays and discuss ways to increase the number of mutually synchronized nano-oscillators. Finally, the potential for applications ranging from microwave signal sources/detectors and ultrafast spectrum analyzers to neuromorphic computing elements and Ising machines is discussed together with the specific electronic circuitry that has been designed so far to harness this potential.

cond-mat.mes-hall↗

arXiv2023

A 50-spin surface acoustic wave Ising machine

Time-multiplexed Spinwave Ising Machines (SWIMs) have unveiled a route towards miniaturized, low-cost, and low-power solvers of combinatorial optimization problems. While the number of supported spins is limited by the nonlinearity of the spinwave dispersion, other collective excitations, such as surface acoustic waves (SAWs), offer a linear dispersion. Here, we demonstrate an all-to-all, fully FPGA reprogrammable, 50-spin surface acoustic wave-based Ising machine (SAWIM), using a 50-mm-long Lithium Niobate SAW delay line, off-the-shelf microwave components, and a low-cost FPGA. The SAWIM can solve any 50-spin MAX-CUT problem, with arbitrary coupling matrices, in less than 340 $μ$s consuming only 0.62 mJ, corresponding to close to 3000 solutions per second and a figure of merit of 1610 solutions/W/s. We compare the SAWIM computational results with those of a 100-spin optical Coherent Ising machine and find a higher probability of solution. Moreover, we demonstrate that there is an optimum overall coupling strength between spins at which the probability of the exact solution reaches 100%. The SAWIM illustrates the general merits of solid state wave-based time-multiplexed Ising machines in the microwave domain as versatile platforms for commercially feasible high-performance solvers of combinatorial optimization problems.

cond-mat.mes-hall↗

arXiv2023

Global biasing using a Hardware-based artificial Zeeman term in Spinwave Ising Machines

A spinwave Ising machine (SWIM) is a newly proposed type of time-multiplexed hardware solver for combinatorial optimization that employs feedback coupling and phase sensitive amplification to map an Ising Hamiltonian into phase-binarized propagating spin-wave RF pulses in an Yttrium-Iron-Garnet (YIG) film. In this work, we increase the mathematical complexity of the SWIM by adding a global Zeeman term to a 4-spin MAX-CUT Hamiltonian using a continuous external electrical signal with the same frequency as the spin pulses and phase locked with with one of the two possible states. We are able to induce ferromagnetic ordering in both directions of the spin states despite antiferromagnetic pairwise coupling. Embedding a planar antiferromagnetic spin system in a magnetic field has been proven to increase the complexity of the graph associated to its Hamiltonian and thus this straightforward implementation helps explore higher degrees of complexity in this evolving solver.

cond-mat.mes-hall↗

arXiv2023

Ultra-low current 10 nm spin Hall nano-oscillators

Nano-constriction based spin Hall nano-oscillators (SHNOs) are at the forefront of spintronics research for emerging technological applications such as oscillator-based neuromorphic computing and Ising Machines. However, their miniaturization to the sub-50 nm width regime results in poor scaling of the threshold current. Here, we show that current shunting through the Si substrate is the origin of this problem and study how different seed layers can mitigate it. We find that an ultra-thin Al$_{2}$O$_{3}$ seed layer and SiN (200 nm) coated p-Si substrates provide the best improvement, enabling us to scale down the SHNO width to a truly nanoscopic dimension of 10 nm, operating at threshold currents below 30 $μ$A. In addition, the combination of electrical insulation and high thermal conductivity of the Al$_{2}$O$_{3}$ seed will offer the best conditions for large SHNO arrays, avoiding any significant temperature gradients within the array. Our state-of-the-art ultra-low operational current SHNOs hence pave an energy-efficient route to scale oscillator-based computing to large dynamical neural networks of linear chains or two-dimensional arrays.

cond-mat.mes-hall↗

arXiv2023

Phase noise analysis of mutually synchronized spin Hall nano-oscillators

The reduction of phase noise in electronic systems is of utmost importance in modern communication and signal processing applications and requires an understanding of the underlying physical processes. Here, we systematically study the phase noise in mutually synchronized chains of nano-constriction spin Hall nano-oscillators (SHNOs). We find that longer chains have improved phase noise figures at low offset frequencies (1/f noise), where chains of two and ten mutually synchronized SHNOs have 2.8 and 6.2 dB lower phase noise than single SHNOs. This is close to the theoretical values of 3 and 10 dB, and the deviation is ascribed to process variations between nano-constrictions. However, at higher offset frequencies (thermal noise), the phase noise unexpectedly increases with chain length, which we ascribe to process variations, a higher operating temperature in the long chains at the same drive current and phase delays in the coupling between nano-constrictions.

cond-mat.mes-hall↗

arXiv2022

A spinwave Ising machine

We demonstrate a spin-wave-based time-multiplexed Ising Machine (SWIM), implemented using a 5 $μ$m thick Yttrium Iron Garnet (YIG) film and off-the-shelf microwave components. The artificial Ising spins consist of 34--68 ns long 3.125 GHz spinwave RF pulses with their phase binarized using a phase-sensitive microwave amplifier. Thanks to the very low spinwave group velocity, the 7 mm long YIG waveguide can host an 8-spin MAX-CUT problem and solve it in less than 4 $μ$s while consuming only 7 $μ$J. Using a real-time oscilloscope, we follow the temporal evolution of each spin as the SWIM minimizes its energy and find both uniform and domain-propagation-like switching of the spin state. The SWIM has the potential for substantial further miniaturization, scalability, speed, and reduced power consumption, and may become a versatile platform for commercially feasible optimization problem solvers with high performance.

cond-mat.mes-hall↗