Neuro-inspired automated lens design
This work addresses inefficiency and limited diversity in lens design for optical engineering, representing a novel automated approach rather than an incremental improvement.
The study tackled the problem of automating complex lens design, which is hindered by non-convex optimization and human expertise, by proposing OptiNeuro, a framework that generates and refines lens structures, achieving quasi-human-level performance with multiple viable candidates.
The highly non-convex optimization landscape of modern lens design necessitates extensive human expertise, resulting in inefficiency and constrained design diversity. While automated methods are desirable, existing approaches remain limited to simple tasks or produce complex lenses with suboptimal image quality. Drawing inspiration from the synaptic pruning mechanism in mammalian neural development, this study proposes OptiNeuro--a novel automated lens design framework that first generates diverse initial structures and then progressively eliminates low-performance lenses while refining remaining candidates through gradient-based optimization. By fully automating the design of complex aspheric imaging lenses, OptiNeuro demonstrates quasi-human-level performance, identifying multiple viable candidates with minimal human intervention. This advancement not only enhances the automation level and efficiency of lens design but also facilitates the exploration of previously uncharted lens architectures.