arXiv subjects

Cody Trevillian

Publications and source records attributed to Cody Trevillian.

7 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↗

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↗

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

Coherent coupling of two remote magnonic resonators mediated by superconducting circuits

We demonstrate microwave-mediated distant magnon-magnon coupling on a superconducting circuit platform, incorporating chip-mounted single-crystal Y$_3$Fe$_5$O$_{12}$ (YIG) spheres. Coherent level repulsion and dissipative level attraction between the magnon modes of the two YIG spheres are demonstrated. The former is mediated by cavity photons of a superconducting resonator, and the latter is mediated by propagating photons of a coplanar waveguide. Our results open new avenues towards exploring integrated hybrid magnonic networks for coherent information processing on a quantum-compatible superconducting platform.

cond-mat.mes-hall↗