Adaptive sampling strategy for tolerance analysis of freeform optical surfaces based on critical ray aiming

TL;DR

This paper introduces an adaptive sampling method called 'Critical Ray Aiming' that improves the efficiency and accuracy of tolerance analysis for freeform optical surfaces by focusing on the most sensitive rays affecting imaging quality.

Contribution

The paper presents a novel adaptive sampling strategy that identifies critical rays to optimize tolerance analysis of freeform surfaces, reducing computational load while maintaining accuracy.

Findings

Efficient tolerance analysis achieved with fewer sampled rays.

Method accurately predicts surface error limits for different FOVs.

Validated on two freeform imaging systems.

Abstract

The tolerance analysis of freeform surfaces plays a crucial role in the development of advanced imaging systems. However, the intricate relationship between surface error and imaging quality poses significant challenges, necessitating dense sampling of featured rays during the computation process to ensure an accurate tolerance for different fields of view (FOVs). Here, we propose an adaptive sampling strategy called "Critical Ray Aiming" for surface tolerance analysis. By identifying the most sensitive ray to wave aberration at each surface point, our methodology facilitates flexible sampling of the FOVs and entrance pupil (EP), achieving computational efficiency without compromising accuracy in determining tolerable surface error. We demonstrate the effectiveness of our method through tolerance analysis of two different freeform imaging systems.