基于人工神经网络方法的自由曲面光学系统装调

Freeform Optical System Alignment Based on Artificial Neural Networks

自由曲面光学表面越来越广泛地应用于光学工程领域,传统的计算机辅助装调方法已难以指导含自由曲面的复杂光学系统装调。提出了一种新型人工神经网络计算方法,用以辅助光学系统装调。介绍了神经网络方法的数学模型,并提供了两个模拟装调实例以验证此方法的实用性。模拟装调结果表明,以系统出瞳波前的光程差分布和泽尼克多项式拟合系数作为像质指示参量,经由神经网络方法计算得到的失调量均方根误差小于7.04%;神经网络方法可用于指导自由曲面光学系统的精确装调。

Freeform surfaces freeform optical surfaces are widely utilized in optical engineering domain, while the traditional computer-aided alignment methods fail to guide the alignment of the optical system containing. A novel method using artificial neural networks is proposed to assist the alignment of optical system. The logical model of alignment with neural networks is introduced, and two alignment simulation examples are taken to verify the practicability of this method. The alignment results show that when the optical path difference distribution or the simulated Zernike polynomial coefficients of the system exit pupil wavefront are used as imaging quality parameters,the root-mean-square errors of misalignment parameters computed by the neural network method are less than 7.04%. The neural network method provides a certain reference to alignment of freeform surface systems.