Back-propagation-assisted inverse design of structured light fields for given profiles of optical force

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.

Automated summary

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. Despite decades of research on OF, it is little studied due to the need for high dimensional optimization and lengthy calculations. In this work, we present the first attempt to attack this inverse design problem using the back-propagation algorithm and a machine learning framework. We provide efficient expressions of OF, which depend on polynomial and trigonometric functions, for the engineered parameters governing the structured light field. Two illustrative examples are presented, showcasing the reproduction of a predefined spatial pattern of OF and a negative longitudinal OF in a transversely trapping area.