arXiv subjects

Steven Louis

Publications and source records attributed to Steven Louis.

16 recordsLinked to original sources

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↗

arXiv2026

Temporal magnon-qubit Mach-Zehnder interferometer

A temporal magnon-qubit Mach-Zehnder (MZ) interferometer is proposed. The interferometer is based on controllable entanglement of a microwave qubit and a magnonic state, achieved by application of a pulsed magnetic field playing the role of a magnon-qubit temporal "beam splitter". Analogous to a typical MZ interferometer, the generated interference pattern of the final qubit population carries information about the magnon dynamics. One important application of the proposed scheme is the study of single magnon decoherence. Interestingly, this scheme allows one to independently determine rates of two possible decoherence channels. This may help enable single magnon state applications and answer fundamental questions of quasi-particle decoherence at single quantum levels.

cond-mat.mes-hall↗

arXiv2026

Time-Domain Two-Magnon Interference Enabled by a Tunable Beamsplitter

This letter presents a model system for controllable two-magnon interference in the time domain. This two-magnon interference, i.e., a magnonic analog to the photonic Hong-Ou-Mandel effect, is supported by a tunable magnonic beamsplitter operation formed in a hybrid cavity magnonic system comprising a pair of mutually coupled magnon modes. By applying a time-dependent magnetic field, magnons can be excited independently in each mode and subsequently brought into interaction, shifting from independent to collective oscillations, to realize a controllable magnonic beamsplitter. When the beamsplitter operation is applied to an initially unentangled two-magnon state, a maximally entangled magnonic $N00N$ state with tunable phase sensitivity is produced. These findings suggest that two-magnon interference in hybrid cavity magnonic systems may enable novel quantum metrological devices to study fundamental magnon dynamics and contribute to developing hybrid magnonic quantum computing architectures.

quant-ph↗

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

Equivalent Electric Model of a Macrospin

Dynamics of a ferromagnetic macrospin (e.g., a free layer of a magnetic tunnel junction (MTJ)) can be described in terms of equivalent capacitor charge $Q$ and inductor flux $\Phi$, in a manner similar to a standard electric LC circuit, but with strongly nonlinear and coupled capacitance and inductance. This description allows for the inclusion of Gilbert damping and spin transfer torques and yields a relatively simple equivalent electric circuit, which can be easily modeled in LTspice or other electrical engineering software. It allows one to easily simulate advanced electrical circuits containing MTJs and conventional electronic components in standard simulation software.

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↗

arXiv2024

Spintronic Neuron Using a Magnetic Tunnel Junction for Low-Power Neuromorphic Computing

This paper proposes a novel spiking artificial neuron design based on a combined spin valve/magnetic tunnel junction (SV/MTJ). Traditional hardware used in artificial intelligence and machine learning faces significant challenges related to high power consumption and scalability. To address these challenges, spintronic neurons, which can mimic biologically inspired neural behaviors, offer a promising solution. We present a model of an SV/MTJ-based neuron which uses technologies that have been successfully integrated with CMOS in commercially available applications. The operational dynamics of the neuron are derived analytically through the Landau-Lifshitz-Gilbert-Slonczewski (LLGS) equation, demonstrating its ability to replicate key spiking characteristics of biological neurons, such as response latency and refractive behavior. Simulation results indicate that the proposed neuron design can operate on a timescale of about 1 ns, without any bias current, and with power consumption as low as 50 uW.

physics.app-ph↗

arXiv2023

Pattern recognition using spiking antiferromagnetic neurons

Spintronic devices offer a promising avenue for the development of nanoscale, energy-efficient artificial neurons for neuromorphic computing. It has previously been shown that with antiferromagnetic (AFM) oscillators, ultra-fast spiking artificial neurons can be made that mimic many unique features of biological neurons. In this work, we train an artificial neural network of AFM neurons to perform pattern recognition. A simple machine learning algorithm called spike pattern association neuron (SPAN), which relies on the temporal position of neuron spikes, is used during training. In under a microsecond of physical time, the AFM neural network is trained to recognize symbols composed from a grid by producing a spike within a specified time window. We further achieve multi-symbol recognition with the addition of an output layer to suppress undesirable spikes. Through the utilization of AFM neurons and the SPAN algorithm, we create a neural network capable of high-accuracy recognition with overall power consumption on the order of picojoules.

cs.NE↗

arXiv2022

Artificial neurons based on antiferromagnetic auto-oscillators as a platform for neuromorphic computing

Spiking artificial neurons emulate the voltage spikes of biological neurons, and constitute the building blocks of a new class of energy efficient, neuromorphic computing systems. Antiferromagnetic materials can, in theory, be used to construct spiking artificial neurons. When configured as a neuron, the magnetizations in antiferromagnetic materials have an effective inertia that gives them intrinsic characteristics that closely resemble biological neurons, in contrast with conventional artificial spiking neurons. It is shown here that antiferromagnetic neurons have a spike duration on the order of a picosecond, a power consumption of about 10^-3 pJ per synaptic operation, and built-in features that directly resemble biological neurons, including response latency, refraction, and inhibition. It is also demonstrated that antiferromagnetic neurons interconnected into physical neural networks can perform unidirectional data processing even for passive symmetrical interconnects. Flexibility of antiferromagnetic neurons is illustrated by simulations of simple neuromorphic circuits realizing Boolean logic gates and controllable memory loops.

cond-mat.mes-hall↗

arXiv2022

Tunable magnetically induced transparency spectra in magnon-magnon coupled Y3Fe5O12/permalloy bilayers

Hybrid magnonic systems host a variety of characteristic quantum phenomena such as the magnetically-induced transparency (MIT) and Purcell effect, which are considered useful for future coherent quantum information processing. In this work, we experimentally demonstrate a tunable MIT effect in the Y3Fe5O12(YIG)/Permalloy(Py) magnon-magnon coupled system via changing the magnetic field orientations. By probing the magneto-optic effects of Py and YIG, we identify clear features of MIT spectra induced by the mode hybridization between the uniform mode of Py and the perpendicular standing spin-wave modes of YIG. By changing the external magnetic field orientations, we observe a tunable coupling strength between the YIG's spin-wave modes and the Py's uniform mode, upon the application of an out-of-plane magnetic field. This observation is theoretically interpreted by a geometrical consideration of the Py and YIG magnetization under the oblique magnetic field even at a constant interfacial exchange coupling. Our findings show high promise for investigating tunable coherent phenomena with hybrid magnonic platforms.

cond-mat.mtrl-sci↗

arXiv2022

Hybrid magnonics for short-wavelength spin waves facilitated by a magnetic heterostructure

Recent research on hybrid magnonics has been restricted by the long magnon wavelengths of the ferromagnetic resonance modes. We present an experiment on the hybridization of 250-nm wavelength magnons with microwave photons in a multimode magnonic system consists of a planar cavity and a magnetic bilayer. The coupling between magnon modes in the two magnetic layers, i.e., the uniform mode in Permalloy (Py) and the perpendicular standing spin waves (PSSWs) in YIG, serves as an effective means for exciting short-wavelength PSSWs, which is further hybridized with the photon mode of the microwave resonator. The demonstrated magnon-photon coupling approaches the superstrong coupling regime, and can even be achieved near zero bias field.

cond-mat.mes-hall↗

arXiv2021

Ultra-fast GHz-range swept-tuned spectrum analyzer with 20 ns temporal resolution based on a spin-torque nano-oscillator with a uniformly magnetized 'free' layer

The advantage of an ultra-fast frequency-tunability of spin-torque nano-oscillators (STNOs) that have large (> 100MHz) relaxation frequency of amplitude fluctuations is exploited to realize ultra-fast wide-band time-resolved spectral analysis at nanosecond time scale with the frequency resolution limited only by the 'bandwidth' theorem. The demonstration is performed with an STNO generating in the 9 GHz frequency range, and comprised of a perpendicular polarizer and a perpendicularly and uniformly magnetized 'free' layer. It is shown that such a uniform-state STNO-based spectrum analyzer can efficiently perform spectral analysis of frequency-agile signals with rapidly varying frequency components.

physics.app-ph↗

arXiv2018

Ultra-fast wide band spectrum analyzer based on a rapidly tuned spin-torque nano-oscillator

A spintronic method of ultra-fast broadband microwave spectrum analysis is proposed. It uses a rapidly tuned spin torque nano-oscillator (STNO), and does not require injection locking. This method treats an STNO generating a microwave signal as an element with an oscillating resistance. When an external signal is applied to this "resistor" for analysis, it is mixed with the signal generated by the STNO. The resulting mixed voltage contains the "sum" and "difference" frequencies, and the latter produces a DC component when the external frequency matches the frequency generated by the STNO. The mixed voltage is processed using a low pass filter to exclude the "sum" frequency components, and a matched filter to exclude the dependence of the resultant DC voltage on the phase difference between the two signals. It is found analytically and by numerical simulation, that the proposed spectrum analyzer has a frequency resolution at a theoretical limit in a real-time scanning bandwidth of 10~GHz, and a frequency scanning rate above 1~GHz/ns, while remaining sensitive to signal power as low as the Johnson-Nyquist thermal noise floor.

cond-mat.mes-hall↗

arXiv2018

Spintronic nano-scale harvester of broadband microwave energy

The harvesting of ambient radio-frequency (RF) energy is an attractive and clean way to realize the idea of self-powered electronics. Here we present a design for a microwave energy harvester based on a nanoscale spintronic diode (NSD). This diode contains a magnetic tunnel junction with a canted magnetization of the free layer, and can convert RF energy over the frequency range from 100 MHz to 1.2 GHz into DC electric voltage. An attractive property of the developed NSD is the generation of an almost constant DC voltage in a wide range of frequencies of the external RF signals. We further show that the developed NSD provides sufficient DC voltage to power a low-power nanodevice - a black phosphorus photo-sensor. Our results demonstrate that the developed NSD could pave the way for using spintronic detectors as building blocks for self-powered nano-systems, such as implantable biomedical devices, wireless sensors, and portable electronics.

physics.app-ph↗

arXiv2017

Low Power Microwave Signal Detection With a Spin-Torque Nano-Oscillator in the Active Self-Oscillating Regime

A spin-torque nano-oscillator (STNO) driven by a ramped bias current can perform spectrum analysis quickly over a wide frequency bandwidth. The STNO spectrum analyzer operates by injection locking to external microwave signals and produces an output DC voltage $V_{\rm dc}$ that temporally encodes the input spectrum. We found, via numerical analysis with a macrospin approximation, that an STNO is able to scan a $10~\rm GHz$ bandwidth in less than $100~\rm ns$ (scanning rate $R$ exceeds $100~\rm MHz/ns$). In contrast to conventional quadratic microwave detectors, the output voltage of the STNO analyzer is proportional to the amplitude of the input microwave signal $I_{\rm rf}$ with sensitivity $S = dV_{\rm dc}/dI_{\rm rf} \approx 750~\rm mV/mA$. The minimum detectable signal of the analyzer depends on the scanning rate $R$ and, at low $R \approx 1~\rm MHz/ns$, is about $1~\rm pW$.

cond-mat.mes-hall↗

arXiv2016

Bias-free spin-wave phase shifter for magnonic logic

A design of a magnonic phase shifter operating without an external bias magnetic field is proposed. The phase shifter uses a localized collective spin wave mode propagating along a domain wall "waveguide" in a dipolarly-coupled magnetic dot array existing in a chessboard antiferromagnetic (CAFM) ground state. It is demonstrated numerically that remagnetization of a single magnetic dot adjacent to the domain wall waveguide introduces a controllable phase shift in the propagating spin wave mode without significant change of the mode amplitude. It is also demonstrated that a logic XOR gate can be realized in the same system.

cond-mat.mes-hall↗