Category: PRIN 2022 PHERMIAC

Observation of Lump Solitons — after 50 years

Solitons are the cornerstone of nonlinear physics. The integrability of nonlinear equations is the basis of this universal concept. However, most multidimensional systems lack integrability, a fundamental limitation that challenges the existence of solitons in high dimensions. A remarkable exception would be the lump soliton, a two-dimensional solution of the Kadomtsev-Petviashvili (KP) equation with the unique property of propagating unperturbed in three-dimensional space. Due to the difficulty of implementing the KP dynamics in any physical system, lump solitons have never been observed. Here, we report the first experimental observation of the lump soliton. The lump is realized in nonlinear optics, in a photorefractive crystal under the action of paraxial diffraction and defocusing nonlinearity, ruled by the (2+1)⁢D nonlinear Schrödinger (NLS) equation. We tailor the input field shape and the nonlinearity to realize the hydrodynamic KP integrable regime of the NLS equation. The lump emerges as a self-localized wave that propagates unaltered with a transverse velocity. We confirm its integrable nature by reporting, for the first time, the elastic collision of lumps in two dimensions. As the first experimental evidence of integrable solitons in high dimensions, our observation paves the way for a new era in the study of nonlinear systems.

Featured in Physics, Editors’suggestion

https://physics.aps.org/articles/v19/s22

https://phys.org/news/2026-01-physicists-resilient-3d-solitons-lab.html

The solution to energy demanding artificial intelligence : optical neural networks with solar cells

https://www.researching.cn/articles/OJ930a39ae0105db58

https://opg.optica.org/prj/abstract.cfm?doi=10.1364/PRJ.542564

Optical neural networks (ONNs) are a class of emerging computing platforms that leverage the properties of light to perform ultra-fast computations with ultra-low energy consumption. ONNs often use CCD cameras as the output layer. In this work, we propose the use of perovskite solar cells as a promising alternative to imaging cameras in ONN designs. Solar cells are ubiquitous, versatile, highly customizable, and can be fabricated quickly in laboratories. Their large acquisition area and outstanding efficiency enable them to generate output signals with a large dynamic range without the need for amplification. Here we have experimentally demonstrated the feasibility of using perovskite solar cells for capturing ONN output states, as well as the capability of single-layer random ONNs to achieve excellent performance even with a very limited number of pixels. Our results show that the solar-cell-based ONN setup consistently outperforms the same setup with CCD cameras of the same resolution. These findings highlight the potential of solar-cell-based ONNs as an ideal choice for automated and battery-free edge-computing applications.

https://doi.org/10.1364/PRJ.542564