Xiaoye Chen

Gigahertz excitations of magnetic films are widely explored for energy‐efficient, high‐frequency microelectronics. The advent of nanoscale chiral spin textures (CSTs) with topological dynamics promises novel resonance characteristics. However, prior works on technologically relevant chiral multilayers encountered key material constraints, precluding the realization of functional CST resonances. We address this by engineering a minimally damped, strongly chiral multilayer with a robust broadband resonance spectrum. Microwave spectroscopy, Lorentz microscopy, and simulations elucidate contrasting resonance features on either side of zero magnetic field arising from distinct irreversible CST transitions. A simple analytical model can quantitatively describe these robust inter‐textural resonances over the entire field‐frequency range. Crucially, in situ CST reconfigurability enables analog tunability of the resonant dispersion ‐ with wide‐band, deterministic, non‐linear (or linear) modulation via the input knob. Our work unlocks the microwave potential of multilayer CSTs by leveraging their unique thermodynamics. It opens the door to fabrication‐free reconfigurable magnonics, toward broadband transmission and unconventional computing.

The interplay of magnetic interactions in chiral multilayer films gives rise to nanoscale topological spin textures that form attractive elements for next-generation computing. Quantifying these interactions requires several specialized, time-consuming, and resource-intensive experimental techniques. Imaging of ambient domain configurations presents a promising avenue for high-throughput extraction of parent magnetic interactions. Here, we present a machine learning (ML)-based approach to simultaneously determine the key magnetic interactions-symmetric exchange, chiral exchange, and anisotropy-governing the chiral domain phenomenology in multilayers, using a single binarized image of domain configurations. Our convolutional neural network model, trained and validated on over 10,000 domain images, achieved R-2 > 0.85 in predicting the parameters and independently learned the physical interdependencies between magnetic parameters. When applied to microscopy data acquired across samples, our model-predicted parameter trends are consistent with those of independent experimental measurements. These results establish ML-driven techniques as valuable, high-throughput complements to conventional determination of magnetic interactions and serve to accelerate materials and device development for nanoscale electronics.

Competing interactions produce finite-size textures in myriad condensed matter systems, typically forming elongated stripe or round bubble domains. Transitions between stripe and bubble phases, driven by field or temperature, are expected to be reversible in nature. Here we report on the distinct character of the analogous transition for nanoscale spin textures in chiral Co/Pt-based multilayer films, known to host Néel skyrmions, using microscopy, magnetometry, and micromagnetic simulations. Upon increasing field, individual stripes fission into multiple skyrmions, and this transition exhibits a macroscopic signature of irreversibility. Crucially, upon field reversal, the skyrmions do not fuse back into stripes, with many skyrmions retaining their morphology down to zero field. Both the macroscopic irreversibility and the microscopic zero-field skyrmion density are governed by the thermodynamic material parameter determining chiral domain stability. These results establish the thermodynamic and microscopic framework underlying ambient skyrmion generation and stability in chiral multilayer films and provide immediate directions for their functionalization in devices.