All‐Optical Multi‐Order Multiplexing Differentiation Based on Dynamic Liquid Crystals

Abstract

With the advantages of parallel processing, high‐speed operation, and ultra‐low power consumption, optical computing presents immense potential in this age of information explosion. As a fundamental optical computing component, optical differentiator has extensive applications, particularly excelling in the real‐time processing and recognition of high‐frame‐rate image information. An ideal optical differentiator needs to adapt to different application scenarios, such as arbitrary operating wavelengths, complex or even transparent targets. Furthermore, performing differential operations of varying orders on target optical field can extract multi‐level information. Unfortunately, most current differential computing devices can only meet part of the requirements, which limits the attained information dimensions and hinders the development of their practical application. Herein, a universal platform based on liquid crystals (LCs) is proposed to achieve 0th‐, 1st‐, and 2nd‐order differential operations of amplitude‐typed or even phase‐typed objects within a broadband range. The switching of different differentiation orders can be accomplished by straightforward adjusting the applied voltages of the differentiator. This approach is capable of extracting more feature information including bright‐field, outline, and edges images of versatile targets. With advanced characteristics of multi‐dimension, broadband and multifunction, the work can flourish applications in fields of image processing, biomedical imaging and optical analog computation, etc.