Neuro-inspired automated lens design

2025-10-10

AI Summary

Modern optical lens design faces highly non-convex optimization challenges, with conventional approaches relying heavily on expert intuition—resulting in low efficiency and limited structural diversity. This paper proposes a novel automated design framework inspired by synaptic pruning in neural development, the first to integrate biologically plausible structural generation with progressive pruning optimization for complex aspheric imaging lenses. Our method comprises three synergistic stages: (1) initial topological diversity sampling, (2) gradient-driven parameter refinement, and (3) iterative pruning of underperforming configurations—enabling fully end-to-end automation. Compared to state-of-the-art methods, our framework achieves high imaging fidelity (MTF ≥ 0.6 at Nyquist frequency) while substantially expanding the exploratory architectural space. It successfully generates multiple high-performance lens designs competitive with expert-crafted counterparts, thereby overcoming the longstanding performance–diversity trade-off bottleneck in automated optical design.

Technology Category

• Search and Optimization: Non-convex Optimization
• Computer Vision: Learning & Optimization for CV
• Machine Learning: Optimization

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines

Abstract

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.

Problem

• Automating complex lens design to overcome human expertise limitations
• Addressing non-convex optimization challenges in modern lens systems
• Generating diverse high-performance lenses through neuro-inspired pruning

Innovation

• Neuro-inspired automated lens design framework
• Progressively eliminates and refines lens candidates
• Fully automates complex aspheric lens design