Modeling nonlinear optical interactions of focused beams in bulk crystals and thin films: A phenomenological approach

TL;DR

This paper introduces a semi-analytical model for nonlinear optical interactions in focused beams within bulk crystals and thin films, simplifying data interpretation and reducing computational costs while maintaining accuracy.

Contribution

It presents a novel, easy-to-use modeling approach based on Gaussian beam solutions that extends coherence length concepts for better analysis of nonlinear optical responses.

Findings

Excellent agreement with experimental data for second- and third-harmonic generation

Model reduces computational time by over three orders of magnitude compared to rigorous simulations

Performs comparably well to more complex methods in accuracy

Abstract

Coherent nonlinear optical micro-spectroscopy is a frequently used tool in modern material science, as it is sensitive to many different local observables, which comprise, among others, crystal symmetry and vibrational properties. The richness in information, however, may come with challenges in data interpretation, as one has to disentangle the many different effects like multiple reflections, phase jumps at interfaces, or the influence of the Guoy-phase. In order to facilitate interpretation, the work presented here proposes an easy-to-use semi-analytical modeling ansatz, that bases upon known analytical solutions using Gaussian beams. Specifically, we apply this ansatz to compute nonlinear optical responses of (thin film) optical materials. We try to conserve the meaning of intuitive parameters like the Gouy-phase and the nonlinear coherent interaction length. In particular, the concept of coherence length is extended, which is a must when using focal beams. The model is subsequently applied to exemplary cases of second-harmonic and third-harmonic generation. We observe a very good agreement with experimental data and furthermore, despite the constraints and limits of the analytical ansatz, our model performs similarly well as when using more rigorous simulations. However, it outperforms the latter in terms of computational power, requiring more than three orders less computational time and less performant computer systems.