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

Projektinformationen

3D NOAM

ID Finanzhilfevereinbarung: 101044797

DOI

10.3030/101044797

EK-Unterschriftsdatum 27 April 2022

Startdatum 1 Januar 2023

Enddatum 31 Dezember 2027

Finanziert unter

• European Research Council (ERC)

Gesamtkosten

€ 3 000 000,00

EU-Beitrag

€ 3 000 000,00

Koordiniert durch

TEL AVIV UNIVERSITY
Israel

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

Berichtszeitraum: 2023-01-01 bis 2025-06-30

Zusammenfassung vom Kontext und den Gesamtzielen des Projekts

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.

Arbeit, die ab Beginn des Projekts bis zum Ende des durch den Bericht erfassten Berichtszeitraums geleistet wurde, und die wichtigsten bis dahin erzielten Ergebnisse

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.

Fortschritte, die über den aktuellen Stand der Technik hinausgehen und voraussichtliche potenzielle Auswirkungen (einschließlich der bis dato erzielten sozioökonomischen Auswirkungen und weiter gefassten gesellschaftlichen Auswirkungen des Projekts)

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