arXiv subjects

Maryam Massouras

Publications and source records attributed to Maryam Massouras.

4 recordsLinked to original sources

arXiv2026

Nonlinear mode interactions under parametric excitation in a YIG microdisk

A pair of quantized spin-wave modes is driven by two-tone parallel pumping in a YIG microdisk. The nonlinear dynamics is experimentally investigated by probing the resulting steady state, which is found to critically depend on the chosen pair of modes, the detuning between the pump frequencies and the modes parametric resonance, as well as the temporal sequence of the two rf tones. A general theory of parametric excitation in confined structures based on magnetization normal modes is developed and quantitatively accounts for the observed dependence and non-commutative behaviors, which emerge from the interplay between the self and mutual nonlinear frequency shifts of the spin-wave modes. Owing to its high degree of external controllability and scalability to larger sets of modes, this dynamical system provides a model platform for exploring nonlinear phenomena and a promising route toward rf driven state mapping relevant to neuromorphic and unconventional computing.

cond-mat.mes-hall↗

arXiv2026

Multiple three-magnon splittings in bismuth yttrium iron garnet nanostructures

We experimentally demonstrate the generation of multiple three-magnon splitting processes in an in-plane magnetized submicron Bi-YIG disk using micro-focused Brillouin light scattering. The low magnetic damping and strong magneto-optical response of BiYIG enable the detection of nonlinear spin-wave interactions at low threshold powers. By tuning the in-plane static magnetic field, excitation frequency, and power, we observe the generation of three pairs of secondary modes symmetrically distributed around half the excitation frequency. Time-resolved BLS measurements present temporal dynamics and threshold behavior associated with the successive activation of three-magnon pairs.

cond-mat.mes-hall↗

arXiv2024

Mode-resolved micromagnetics study of parametric spin wave excitation in thin-film disks

We present a computational study of the parametric excitation of spin waves in thin film disks with a mode-resolved approach. The method involves projecting out the time-dependent magnetization, computed using micromagnetics simulations, onto the spatial profile of the eigenmodes that are obtained from the linearization of the equations of motion. Unlike spectral analysis in the frequency domain, the projection allows for the analysis of transient mode dynamics under parametric excitation. We apply this method to parallel pumping of quantized spin wave modes in in-plane magnetized thin-film disks, where phenomena such as frequency pulling, mutual phase locking, and higher-order magnon scattering processes are identified.

cond-mat.mes-hall↗

arXiv2021

Tunable stochasticity in an artificial spin network

Metamaterials present the possibility of artificially generating advanced functionalities through engineering of their internal structure. Artificial spin networks, in which a large number of nanoscale magnetic elements are coupled together, are promising metamaterial candidates that enable the control of collective magnetic behavior through tuning of the local interaction between elements. In this work, the motion of magnetic domain-walls in an artificial spin network leads to a tunable stochastic response of the metamaterial, which can be tailored through an external magnetic field and local lattice modifications. This type of tunable stochastic network produces a controllable random response exploiting intrinsic stochasticity within magnetic domain-wall motion at the nanoscale. An iconic demonstration used to illustrate the control of randomness is the Galton board. In this system, multiple balls fall into an array of pegs to generate a bell-shaped curve that can be modified via the array spacing or the tilt of the board. A nanoscale recreation of this experiment using an artificial spin network is employed to demonstrate tunable stochasticity. This type of tunable stochastic network opens new paths towards post-Von Neumann computing architectures such as Bayesian sensing or random neural networks, in which stochasticity is harnessed to efficiently perform complex computational tasks.

cond-mat.mes-hall↗