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Analysis and optimal design of diffractive optical elements.

机译:衍射光学元件的分析和优化设计。

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摘要

The problem we study arose in an industrial application. For an optical system, Diffractive Optical Elements (DOE) are used to produce a certain light intensity pattern in the near field. Of our particular interest is an Inverse problem: given a target image, determine the DOE configuration, e.g. thickness, that would produce this image. The problem can be complicated by specific constraints such as finite number of thickness levels that the DOE can have.; Diffraction theory and Green's function approach are applied to construct a mathematical model for the light propagating through the DOE. Asymptotic methods of stationary phase and multiple-scale analysis are used to derive analytic solutions for periodic and quasi-periodic cases. These analytical expressions do not involve integration, save computational resources, and allow us to solve the Inverse problem analytically. Numerical results for particular applications are presented.; The Inverse problem can be posed a large optimization problem with finite discrete variables, which can not be solved by traditional methods. We propose Genetic Algorithms based on analogies to natural evolution and representing a combination of random and directed search. A modification of the method that suits better to our problem, the Micro-Genetic Algorithm (MGA), is proposed. The MGA operates on a small set of potential solutions and restarts, using an adaptive mutation scheme, each time the local convergence is achieved. We prove convergence for the MGA using the Markov chain analysis. Numerical results of the MGA optimization are provided.
机译:我们研究的问题出现在工业应用中。对于光学系统,衍射光学元件(DOE)用于在近场中产生特定的光强度图案。我们特别感兴趣的是一个反问题:给定目标图像,确定DOE配置,例如厚度,将产生此图像。该问题可能由于特定的约束而变得复杂,例如DOE可以具有的有限数量的厚度级别。应用衍射理论和格林函数方法为通过DOE传播的光建立数学模型。平稳阶段的渐近方法和多尺度分析用于得出周期和准周期情况的解析解。这些解析表达式不涉及集成,节省了计算资源,并允许我们解析地解决逆问题。给出了特定应用的数值结果。逆问题可以提出一个具有有限离散变量的大型优化问题,而传统方法则无法解决。我们提出了基于自然进化的类比并代表随机搜索和定向搜索的组合的遗传算法。提出了一种更适合我们问题的方法的改进,即微遗传算法(MGA)。每次实现局部收敛时,MGA都会使用少量潜在解决方案进行操作,并使用自适应突变方案重新启动。我们使用马尔可夫链分析证明了MGA的收敛性。提供了MGA优化的数值结果。

著录项

  • 作者

    Rudnaya, Svetlana.;

  • 作者单位

    University of Minnesota.;

  • 授予单位 University of Minnesota.;
  • 学科 Mathematics.; Physics Optics.; Computer Science.
  • 学位 Ph.D.
  • 年度 1999
  • 页码 85 p.
  • 总页数 85
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 数学;光学;自动化技术、计算机技术;
  • 关键词

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