arXiv subjects

Edoardo Piccolo

Publications and source records attributed to Edoardo Piccolo.

3 recordsLinked to original sources

arXiv2026

A GPU-based Solver for Polarization Dynamics in Ferroelectric Materials

Ferroelectric materials can be used for the development of multiple device concepts combining non-volatility, small dimensions, low-power actuation, and electrical tunability. Such development demands efficient and precise design of simulation tools describing the polarization texture. However, most existing ferroelectric solvers are CPU-based and rely on simplified electrostatic treatments and reduced-dimensional representations of the polarization field. These approximations limit their ability to capture finite-size and boundary effects and restrict the range of domain structures and domain walls that can be realistically simulated. Here, we present a fully GPU (graphics processing units)-accelerated and scalable numerical solver, named PETASPIN_microelectrics, for computing the full polarization vector field of ferroelectric systems using the Ginzburg-Landau formalism. Our solver incorporates an optimized and validated calculation of the full electrostatic field and enables the parallel execution of multiple simulations. We systematically validated the solver with several benchmark problems, including phase transitions in BaTiO3 and ferroelectric domain wall profiles. Our simulations reproduce temperature-driven hysteretic phase transitions in BaTiO3. We also reproduce hysteresis loops and demonstrate stabilization of a three-dimensional hybrid skyrmion in a PbTiO3/SrTiO3 bilayer system. Our results show quantitative agreement with predictions from an analytical theory and prior experimental studies. The proposed solver provides an efficient, accurate platform for large-scale simulations of ferroelectric materials including stabilization of topological textures supporting predictive modeling for next-generation of ferroelectric device design.

cond-mat.mtrl-sci↗

arXiv2026

Physics-Inspired Probabilistic Computing for Extremely Large-Scale MIMO Detection in Future 6G Wireless Systems

Extremely large-scale multiple-input multiple-output (XL-MIMO) architectures are a key enabler of forthcoming 6G wireless communication networks by allowing high data rates through massive spatial multiplexing. Here, we approach these problems with physics-inspired unconventional computing based on Ising machines (IMs). For binary modulation, probabilistic IMs (PIMs) and oscillator-based IMs achieve optimal ML detection with systems up to 2048x2048 antennas with only 100 iterations, matching optimal sphere decoder performance for computationally treatable sizes and outperforming the minimum mean-square error (MMSE) industrial standard. For M-QAM up to 256, a generalized PIM-inspired framework, based on d-dimensional probabilistic variables (p-dits) that directly encode QAM symbols, shows low bit-error-rate across sizes up to 256x256 antennas, outperforming or matching MMSE with reduced algorithmic complexity. Unlike the binary mapping, the p-dit interaction matrix is independent of the QAM order, enabling adaptive MIMO modulation. These results show a promising scalable paradigm for XL MIMO detection in future 6G networks.

cs.IT↗

arXiv2026

Data-Driven Estimation of the interfacial Dzyaloshinskii-Moriya Interaction with Machine Learning

Machine learning offers powerful tools to support experimental techniques, particularly for extracting latent features from large datasets. In magnetic materials, accurately estimating the interfacial Dzyaloshinskii-Moriya interaction strength remains challenging, as existing experimental methods often rely on indirect measurements and can yield inconsistent results across techniques. Because this interaction is often extracted experimentally from bubble domain expansion, we investigate whether bubble textures alone contain sufficient and reliable information for data driven DMI inference. We therefore develop a compact convolutional neural network trained on a comprehensive micromagnetic dataset of magnetic bubble domains designed to emulate magneto optical Kerr effect imaging, including structural non uniformity, additive noise, and image pixelation. The proposed network demonstrates strong robustness against sample inhomogeneities, noise, and reduced spatial resolution. Furthermore, it exhibits reliable generalization by accurately predicting DMI values outside the trained interval. These results support the use of machine learning as a fast and quantitative tool to characterize magnetic textures with interfacial DMI.

cond-mat.mtrl-sci↗