Efficient and functional optical frequency conversion in 3D Nonlinear Optical Artificial Materials

Información del proyecto

3D NOAM

Identificador del acuerdo de subvención: 101044797

DOI

10.3030/101044797

Fecha de la firma de la CE 27 Abril 2022

Fecha de inicio 1 Enero 2023

Fecha de finalización 31 Diciembre 2027

Financiado con arreglo a

• European Research Council (ERC)

Coste total

€ 3 000 000,00

Aportación de la UE

€ 3 000 000,00

Coordinado por

TEL AVIV UNIVERSITY
Israel

Periodic Reporting for period 1 - 3D NOAM (Efficient and functional optical frequency conversion in 3D Nonlinear Optical Artificial Materials)

Período documentado: 2023-01-01 hasta 2025-06-30

Resumen del contexto y de los objetivos generales del proyecto

Optical frequency conversion in bulk nonlinear crystals is crucial for applications spanning extreme ultraviolet to THz waves, contributing to many fields of science and technology. However, existing nonlinear materials impose significant limitations on integration, miniaturization, and nonlinear interaction control, restricting technological advancement. Recent breakthroughs in nonlinear metasurfaces have demonstrated unprecedented nonlinear properties, vastly outperforming bulk crystals. However, their two-dimensional nature and nanoscale thickness limit their conversion efficiency, with no existing method to overcome these constraints. This project aims to bridge this gap by developing a new class of 3D nano-engineered nonlinear materials. This approach will enhance nonlinear interactions beyond current materials, paving the way for highly efficient, miniaturized optical devices. The expected impacts of the project include: Enhancing Frequency Conversion Efficiency – Achieving superior conversion efficiencies over bulk crystals. Advancing Nanofabrication – Developing scalable, technologically viable 3D nanostructures. Enabling Miniaturization – Creating compact, integrable optical components.

Trabajo realizado desde el comienzo del proyecto hasta el final del período abarcado por el informe y los principales resultados hasta la fecha

During the first research period, we made significant progress in key objectives. In fabrication, we explored three methods to create 3D nonlinear metamaterials, essential for improving optical technologies. In theoretical studies, we developed a computational tool to better understand how these materials interact with light. We successfully designed and tested a special type of two-layered nonlinear surface that enhances light conversion by 16 times, improving efficiency for various optical applications. We also investigated how specific material structures can be engineered to fine-tune light conversion for infrared technologies, which are important for sensing and communication. Additionally, we applied machine learning to optimize the design of these materials, using advanced algorithms to improve light conversion and control diffraction patterns. Another key breakthrough was the creation of hybrid optical lenses that combine traditional focusing properties with enhanced nonlinear light conversion, leading to potential applications in compact imaging systems. Further studies explored new ways to efficiently convert frequencies of light in patterned materials and how strong electric fields can enhance nonlinear optical effects. We also developed a new method for matching light phases in 3D crystal structures, paving the way for future advancements in optical technology.

Avances que van más allá del estado de la técnica e impacto potencial esperado (incluida la repercusión socioeconómica y las implicaciones sociales más amplias del proyecto hasta la fecha)

Advancements in 3D nonlinear metamaterial fabrication enable precise, scalable production of next-generation optical materials, crucial for miniaturized photonic devices. The discovery of enhancement mechanisms for nonlinear optics affects energy conversion and tunable optical devices. Form-birefringent nonlinear metamaterials introduce efficient phase-matching for mid-infrared applications in sensing and communications. Machine learning tools optimize metamaterial design, accelerating material discovery. T