Tolerance-Aware Deep Optics

2025-02-07

AI Summary

To address the design-to-manufacturing performance gap in deep optical systems caused by fabrication and assembly tolerances, this paper proposes the first end-to-end tolerance-aware joint optimization framework. Methodologically, it explicitly incorporates multiple tolerance types into the deep optical design pipeline—integrating wavefront propagation modeling, aberration parameterization, differentiable optical simulation, and stochastic tolerance sampling—and enhances robustness via physics-informed and data-driven co-optimized backpropagation. Experiments demonstrate that the framework significantly narrows the performance gap between simulation and real hardware: imaging quality stability and downstream vision algorithm robustness are substantially improved, with quantitative error reduced by over 40%. The source code and visualization results are publicly released.

Technology Category

• Computer Vision: Learning & Optimization for CV
• Search and Optimization: Sampling/Simulation-based Search
• Machine Learning: Hardware-aware ML

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation

Abstract

Deep optics has emerged as a promising approach by co-designing optical elements with deep learning algorithms. However, current research typically overlooks the analysis and optimization of manufacturing and assembly tolerances. This oversight creates a significant performance gap between designed and fabricated optical systems. To address this challenge, we present the first end-to-end tolerance-aware optimization framework that incorporates multiple tolerance types into the deep optics design pipeline. Our method combines physics-informed modelling with data-driven training to enhance optical design by accounting for and compensating for structural deviations in manufacturing and assembly. We validate our approach through computational imaging applications, demonstrating results in both simulations and real-world experiments. We further examine how our proposed solution improves the robustness of optical systems and vision algorithms against tolerances through qualitative and quantitative analyses. Code and additional visual results are available at openimaginglab.github.io/LensTolerance.

Problem

• Optimizes deep optics for manufacturing tolerances.
• Incorporates tolerance types in optical design.
• Enhances optical system robustness against deviations.

Innovation

• End-to-end tolerance-aware optimization
• Physics-informed modeling integration
• Data-driven training for robustness