Lukáš Flajšman

We investigate spin-wave transport in hybrid two-dimensional magnonic crystals comprising a low-damping yttrium iron garnet (YIG) film coupled to a periodic array of CoFeB nanodisks. Using propagating spin-wave spectroscopy, super-Nyquist magneto-optical Kerr effect microscopy, and micromagnetic simulations, we show that these systems host pronounced, tunable bandgaps that do not originate from conventional Bragg scattering, i.e., from standing-wave quantization restricted to a single axis by translational invariance along the propagation direction. Instead, the gaps arise from hybridization between the fundamental magnonic-crystal mode and in-plane transverse standing modes of the nanodisk array. Their spectral position and width are controlled by the array geometry and by the magnetic state of the nanodisks, including their vortex configuration, which sets both the static and dynamic dipolar coupling to the film. At larger lattice periods, additional gaps emerge from hybridization with modes quantized both transverse and parallel to the propagation direction, reflecting two-dimensional dispersion folding. Our results establish mode hybridization as a versatile mechanism for engineering spin-wave band structures beyond the constraints of conventional Bragg scattering and provide a pathway toward reconfigurable magnonic devices based on dipolar-coupled hybrid architectures.

We report on nonlinear spin-wave dynamics in magnonic Fabry–Pérot resonators composed of yttrium iron garnet films coupled to CoFeB nanostripes. Using super-Nyquist sampling magneto-optical Kerr effect microscopy and micromagnetic simulations, we observe a systematic downshift of the spin-wave transmission gaps as the excitation power increases. This nonlinear behavior occurs at low power levels, reduced by a strong spatial concentration of spin waves within the resonator. The resulting power-dependent transmission enables neuron-like activation behavior and frequency-selective nonlinear spin-wave absorption. Our results highlight magnonic Fabry–Pérot resonators as compact low-power nonlinear elements for neuromorphic magnonic computing architectures.

Magneto-optical materials are indispensable across modern physics, serving as the foundation for precision magnetic sensing, nonreciprocal photonics, and optical isolation technologies. The continual pursuit of materials with high Verdet constants has driven the development of garnet-based compounds exhibiting giant magneto-optical sensitivity. In this work, we report the growth and comprehensive magneto-optical characterization of bismuth-substituted lutetium iron garnet (LuBiIG), a material that combines the large spin–orbit coupling of bismuth with the lattice stability of lutetium iron garnet. LuBiIG films with thicknesses between 85 and 220 nm were grown by pulsed laser deposition and characterized at room temperature over the 500–820 nm wavelength range. The films exhibit an exceptionally high Verdet constant of up to −1.37×108 °m−1 T−1, peaking in the visible spectral range near 520 nm. These results position LuBiIG as a highly sensitive magneto-optical material suitable for advanced cryogenic detection and hybrid quantum applications.

In-materio computing exploits the intrinsic physical dynamics of materials to perform complex computations, enabling low-power, real-time data processing by embedding computation directly within physical layers. Here, we demonstrate a voltage-controlled magneto-ionic device that functions as a reservoir computer capable of forecasting chaotic time series. The device consists of a crossbar structure with a Ta/CoFeB/Ta/MgO/Ta bottom electrode and a LiPON/Pt top electrode. A chaotic Mackey–Glass time series is encoded into a voltage signal applied to the device, while 2D Fourier transforms of voltage-dependent magnetic domain patterns form the output. Performance is influenced by the input rate, smoothing of the output, the number of elements in the reservoir state vector, and the training duration. We identify two distinct computational regimes: Short-term prediction is optimized using smoothed, low-dimensional states with minimal training, whereas prediction around the Mackey–Glass delay time benefits from unsmoothed, high-dimensional states and extended training. Reservoir computing metrics reveal that slower input rates are more tolerant to output smoothing, while faster input rates degrade both memory capacity and nonlinear processing. These findings demonstrate the potential of magneto-ionic systems for neuromorphic computing and offer design principles for tuning performance in response to input signal characteristics.

Active control of low-loss spin-wave propagation at the nanoscale is crucial for energy-efficient magnonic computing. Here, we demonstrate the precise manipulation of spin-wave transport in hybrid waveguides, comprising continuous YIG films overlaid with patterned ferromagnetic metal nanostripes. Our designs enable field-tunable filtering, controllable splitting, and selective 90° redirection of spin waves. Using super-Nyquist sampling magneto-optical Kerr effect (SNS-MOKE) microscopy, corroborated by micromagnetic simulations, we show that spin-wave pathways can be programmed via engineered magnetic field landscapes in the YIG film. Moreover, aligning the magnetization to the diagonal of a cross-shaped waveguide network results in multiple 90° spin-wave redirections with low loss on the nanoscale. This versatile and straightforward approach provides a scalable route to compact, coherent, and reconfigurable magnonic networks, paving the way for integrated wave-based information processing.

The use of spin waves in magnetic thin films at cryogenic temperatures has long been hindered by the lack of a suitable material platform. Yttrium iron garnet (YIG) is the leading candidate, yet it is typically grown on gadolinium gallium garnet (GGG) substrates, which develop a large paramagnetic moment at low temperatures. This substrate effect limits spin-wave propagation. In this work, we demonstrate that thin YIG films grown on yttrium scandium gallium garnet (YSGG) substrates support robust spin-wave propagation in the Damon–Eshbach geometry, measurable down to 2 K under applied magnetic fields up to 150 mT. Compared with YIG/GGG, YIG/YSGG films exhibit narrower ferromagnetic resonance linewidths at low temperatures and are free from the atomic interdiffusion effects that degrade the performance of YIG/GGG systems. These results establish YIG/YSGG thin films as a promising low-temperature platform, overcoming the intrinsic limitations of YIG/GGG and opening new opportunities for scalable magnonic and hybrid quantum devices operating under cryogenic conditions.