Exploitation of multiple incidences spectrometric measurements for thin film reverse engineering

TLDR

The study aims to determine the optical constants and thicknesses of multilayer thin‑film stacks across visible and near‑infrared wavelengths. Optical constants and thicknesses are extracted from transmittance and reflectance spectra measured at multiple incidence angles using a spectrophotometer and a clustering global‑optimization algorithm that evaluates 6–32 variable parameters to resolve the correct physical solution. Employing many incidence angles reduces ambiguous solutions and yields accurate reverse engineering, achieving ~1 nm thickness and 2 × 10⁻³ index precision for a 22‑layer filter.

Abstract

In the present paper we determine the optical constants and thicknesses of multilayer thin film stacks, in the visible and near infrared ranges. These parameters are derived from the transmittance and reflectance spectra measured by a spectrophotometer, for several angles of incidence. Several examples are studied, from a simple single layer structure up to a 22-layer dielectric filter. We show that the use of a large number of incidence angles is an effective means of reducing the number of mathematical solutions and converging on the correct physical solution when the number of layers increases. More specifically, we provide an in-depth discussion of the approach used to extract the index and thickness of each layer, which is achieved by analysing the various mathematical solutions given by a global optimization procedure, based on as little as 6 and as many as 32 variable parameters. The results show that multiple incidences, lead to the true solution for a filter with a large number of layers. In the present study, a Clustering Global Optimization algorithm is used, and is shown to be efficient even for a high number of variable parameters. Our analysis allows the accuracy of the reverse engineering process to be estimated at approximately 1 nm for the thickness, and 2 10(-3) for the index of each layer in a 22-layer filter.