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首页> 外文会议>Applications of Photonic Technology: Closing the Gap between Theory, Development, and Application 7C-Photonics North 2004: Photonic Applications in Telecommunications, Sensors, Software, and Lasers pt.2 >Generalized design of Diffractive Optical Elements using Neural Networks
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Generalized design of Diffractive Optical Elements using Neural Networks

机译：基于神经网络的衍射光学元件的广义设计

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摘要

Diffractive optical elements (DOE) utilize diffraction to manipulate light in optical systems. These elements have a wide range of applications including optical interconnects, coherent beam addition, laser beam shaping and refractive optics aberration correction. Due to the wide range of applications, optimal design of DOE has become an important research problem. In the design of the DOEs, existing techniques utilize the Fresnel diffraction theory to compute the phase at the desired location at the output plane. This process involves solving nonlinear integral equations for which various numerical methods along with robust optimization algorithms exist in literature. However all the algorithms proposed so far assume that the size and the spacing of the elements as independent variables in the design of optimal diffractive gratings. Therefore search algorithms need to be called every time the required geometry of the elements changes, resulting in a computationally expensive design procedure for systems utilizing a large number of DOEs. In this work we have developed a novel algorithm that uses neural networks with possibly multiple hidden layers to overcome this limitation and arrives at a general solution for the design of the DOEs for a given application. Inputs to this network are the spacing between the elements and the input/output planes. The network outputs the phase gratings that are required to obtain the desired intensity at the specified location in the output plane. The network was trained using the back-propagation technique. The training set was generated by using GS algorithm approach as described in literature. The mean square error obtained is comparable to conventional techniques but with much lower computational costs.
机译：衍射光学元件（DOE）利用衍射来操纵光学系统中的光。这些元件具有广泛的应用，包括光学互连，相干光束叠加，激光束整形和折射光学像差校正。由于应用范围广，DOE的优化设计已成为重要的研究问题。在DOE的设计中，现有技术利用菲涅耳衍射理论来计算输出平面上所需位置的相位。该过程涉及求解非线性积分方程，文献中存在针对非线性积分方程的各种数值方法以及鲁棒的优化算法。然而，到目前为止提出的所有算法都假设在优化衍射光栅的设计中元素的大小和间距是自变量。因此，每当元素的所需几何形状发生变化时，都需要调用搜索算法，从而导致使用大量DOE的系统的计算过程昂贵。在这项工作中，我们开发了一种新颖的算法，该算法使用可能具有多个隐藏层的神经网络来克服此限制，并为给定应用的DOE设计提供了一种通用解决方案。该网络的输入是元素与输入/输出平面之间的间距。网络在输出平面中的指定位置输出获得所需强度所需的相位光栅。该网络是使用反向传播技术进行训练的。训练集是使用文献中所述的GS算法生成的。所获得的均方误差可与常规技术相比，但计算成本低得多。

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