Generalized Jones matrix method for homogeneous biaxial samples - Archive ouverte HAL
Article Dans Une Revue Optics Express Année : 2015

Generalized Jones matrix method for homogeneous biaxial samples

Noe Ortega-Quijano
Julien Fade
Mehdi Alouini

Résumé

The generalized Jones matrix (GJM) is a recently introduced tool to describe linear transformations of three-dimensional light fields. Based on this framework, a specific method for obtaining the GJM of uniaxial anisotropic media was recently presented. However, the GJM of biaxial media had not been tackled so far, as the previous method made use of a simplified rotation matrix that lacks a degree of freedom in the three-dimensional rotation, thus being not suitable for calculating the GJM of biaxial media. In this work we propose a general method to derive the GJM of arbitrarily-oriented homogeneous biaxial media. It is based on the differential generalized Jones matrix (dGJM), which is the three-dimensional counterpart of the conventional differential Jones matrix. We show that the dGJM provides a simple and elegant way to describe uniaxial and biaxial media, with the capacity to model multiple simultaneous optical effects. The practical usefulness of this method is illustrated by the GJM modeling of the polarimetric properties of a negative uniaxial KDP crystal and a biaxial KTP crystal for any three-dimensional sample orientation. The results show that this method constitutes an advantageous and straightforward way to model biaxial media, which show a growing relevance for many interesting applications.
Fichier principal
Vignette du fichier
oe-23-16-20428.pdf (2.56 Mo) Télécharger le fichier
Origine Publication financée par une institution
Loading...

Dates et versions

hal-01181354 , version 1 (08-08-2015)

Identifiants

Citer

Noe Ortega-Quijano, Julien Fade, Mehdi Alouini. Generalized Jones matrix method for homogeneous biaxial samples. Optics Express, 2015, 23 (16), pp.20428-20438. ⟨10.1364/OE.23.020428⟩. ⟨hal-01181354⟩
117 Consultations
571 Téléchargements

Altmetric

Partager

More