Application of artificial neural network to determine the thickness profile of thin film

TL;DR

This paper presents an ANN-based method to estimate thin film thickness from interference patterns, enabling rapid, real-time measurements during deposition.

Contribution

It introduces a novel neural network approach to determine thin film thickness from optical interference data, improving automation and speed over traditional methods.

Findings

High accuracy in thickness estimation from simulated data

Real-time measurement capability demonstrated

Potential for automation in thin film manufacturing

Abstract

In this paper, we introduce a novel artificial neural network (ANN) based scheme to estimate the thickness of thin films deposited on a given substrate. Here we consider the visible interference pattern between a plane wave and a diverging wave reflected from the thin film surface that records the thickness information of the thin film. We assume a uniform thickness profile of the film. However, the thickness increases as the deposition takes place. We extract the intensity data along a line through the center of the interference pattern. We train our network by using a number of such line information of known thickness profiles. The performance of the trained network is then tested by estimating the thickness of unknown surfaces. The numerical simulation results show that the proposed technique can be very much useful for automated measurement of thickness, quickly and in real time, during deposition