Zhao Xiaoshu, Lin Haoze, Chen Huajin, Zheng Hongxia, Ng Jack
State Key Laboratory of Surface Physics and Department of Physics, Fudan University, Shanghai 200433, China.
Department of Computer Science, Columbia University, New York, NY 10027, USA.
Nanophotonics. 2023 May 1;12(11):2019-2027. doi: 10.1515/nanoph-2023-0101. eCollection 2023 May.
Designing a monochromatic spatially-structured light field that recovers the pre-specified profile of optical force (OF) exerted on a particle is an inverse problem. It usually requires high dimensional optimization and involves lengthy calculations, thus remaining little studied despite decades of research on OF. We report here the first attempt to attack this inverse design problem. The modus operandi relies on the back-propagation algorithm, which is facilitated by the currently available machine learning framework, and, in particular, by an exact and efficient expression of OF that shows only polynomial and trigonometric functional dependence on the engineered parameters governing the structured light field. Two illustrative examples are presented in which the inversely designed structured light fields reproduce, respectively, a predefined spatial pattern of OF and a negative longitudinal OF in a transversely trapping area.
设计一个单色空间结构光场,使其恢复施加在粒子上的预定义光力(OF)分布是一个逆问题。这通常需要高维优化,并且涉及冗长的计算,因此尽管对光力进行了数十年的研究,但对此仍研究甚少。我们在此报告首次尝试解决这个逆设计问题。其操作方法依赖于反向传播算法,这得益于当前可用的机器学习框架,特别是通过光力的精确有效表达式,该表达式仅显示对控制结构光场的工程参数的多项式和三角函数依赖关系。给出了两个示例,其中反向设计的结构光场分别在横向捕获区域中重现了预定义的光力空间模式和负纵向光力。