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Updated: May 21, 2026

Characterization of SiN Integrated Optical Phased Arrays on a Wafer-Scale Test Station

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Published on: April 1, 2020

Optimization method for ultra-wide-angle and panoramic optical systems.

Li-Jun Lu¹, Xiao-Yan Hu, Cui-Yuan Sheng

¹Department of Fine Mechanical Engineering, Shanghai University, Shanghai, China. lulijun@shu.edu.cn

|June 15, 2012

Summary

This summary is machine-generated.

This study introduces a new method for analyzing aberrations in ultra-wide-angle and panoramic optical systems. The approach optimizes optical designs, improving performance for fish-eye and omnidirectional imaging systems.

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Area of Science:

Optics and Photonics
Optical Engineering

Background:

Ultra-wide-angle and panoramic optical systems face unique aberration challenges.
Existing methods for analyzing off-axis point object aberrations are complex.

Purpose of the Study:

To develop a unified framework for analyzing and optimizing aberrations in ultra-wide-angle and panoramic optical systems.
To introduce a novel merit function and optimization algorithm for these systems.

Main Methods:

Derivation of triangular formulae for chief-ray tracing and aberration calculation.
Application of plane-symmetric aberration theory for aperture-ray aberrations.
Development of an optimization program utilizing the differential-evolution algorithm.

Main Results:

Successfully separated and analyzed two primary types of aberrations: aperture-ray and chief-ray.
Developed a novel merit function tailored for ultra-wide-angle and panoramic systems.
Validated the optimization method through the successful redesign of a fish-eye lens and a catadioptric omnidirectional imaging system.

Conclusions:

The proposed aberration analysis and optimization method is effective for ultra-wide-angle and panoramic optical systems.
The developed merit function and algorithm provide a robust approach to optical system design.
This work advances the design capabilities for specialized imaging systems like fish-eye and omnidirectional cameras.