Generalized design of diffractive optical elements using neural networks

Show simple item record Pasupuleti, A. Gopalan, A. Sahin, Ferat Abushagur, M. A. G. 2009-04-08T14:54:11Z 2009-04-08T14:54:11Z 2004-11-16
dc.description Copyright 2004 Society of Photo-Optical Instrumentation Engineers. This paper was published in The Journal of Electronic Imaging and is made available as an electronic reprint (preprint) with permission of SPIE. One print or electronic copy may be made for personal use only. Systematic or multiple reproduction, distribution to multiple locations via electronic or other means, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper are prohibited.  en_US
dc.description.abstract 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. en_US
dc.language.iso en_US en_US
dc.publisher SPIE en_US
dc.relation RIT Scholars content from RIT Digital Media Library has moved from to RIT Scholar Works, please update your feeds & links!
dc.relation.ispartofseries Vol. 5579 en_US
dc.subject Back propagation en_US
dc.subject DOE design en_US
dc.subject Neural networks en_US
dc.title Generalized design of diffractive optical elements using neural networks en_US
dc.type Proceedings en_US
dc.identifier.bibliographiccitation Photonics North 2004: Photonic Applications in Telecommunications, Sensors, Software, and Lasers, edited by J. Armitage, R. Lessard, G. Lampropoulos, Proc. of SPIE Vol. 5579 (SPIE, Bellingham, WA, 2004) doi: 10.1117/12.567191

Files in this item

Files Size Format View
APasupuletiProceedings09-2004.pdf 410.2Kb PDF View/Open

This item appears in the following Collection(s)

Show simple item record

Search RIT DML

Advanced Search