Sequential Three-Dimensional Nonlinear Photonic Structures for Efficient and Switchable Nonlinear Beam Shaping

Chaowei Wang,Pengcheng Chen,Dunzhao Wei,Leran Zhang,Zihang Zhang,Liqun Xu,Yanlei Hu,Jiawen Li,Yong Zhang,Min Xiao,Jiaru Chu,Dong Wu

ACS PHOTONICS（2023）

Univ Sci & Technol China | Nanjing Univ | Sun Yat Sen Univ

Cited 12|Views41

Abstract

Nonlinear beam shaping can be dynamically controlled by temperature or polarization to form switchable nonlinear structured beams, which constitute an unprecedented system for realizing optical encryption at new frequencies and high-dimensional nonclassical light sources. However, previous works were limited by a trade-off between the conversion efficiency and the modulation dimension of the beam. It is still technologically challenging to achieve efficient and switchable multidimensional nonlinear beam shaping. Here, we demonstrate switchable generation of nonlinear structured beams via three-dimensional (3D) nonlinear photonic crystals (NPCs) fabricated by femtosecond laser writing technique with considerable conversion efficiency. The 3D NPCs contain different sequential 3D arrays of spatially modulated chi(2) nonlinearity designed by computer-generated hologram (CGH) along the optical y-axis (LiNbO3 crystal). The output direction of the nonlinear beams can be changed by the carrier frequencies, while their efficiencies can be increased by the quasi-phase matching condition.

Translated text

Key words

femtosecond laser writing,switchable nonlinear beam shaping,quasi-phase-matching,wavelength-multiplexed technique

求助PDF

上传PDF

Bibtex

AI Read Science

AI Summary

AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.

Example

Background

Key content

Introduction

Methods

Results

Related work

Fund

Key content

Pretraining has recently greatly promoted the development of natural language processing (NLP)
We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance

Upload PDF to Generate Summary

Must-Reading Tree

Example

Generate MRT to find the research sequence of this paper