  Over the recent years, there has been an extensive adoption of Machine
Learning (ML) in a plethora of real-world applications, ranging from computer
vision to data mining and drug discovery. In this paper, we utilize ML to
facilitate efficient film fabrication, specifically Atomic Layer Deposition
(ALD). In order to make advances in ALD process development, which is utilized
to generate thin films, and its subsequent accelerated adoption in industry, it
is imperative to understand the underlying atomistic processes. Towards this
end, in situ techniques for monitoring film growth, such as Spectroscopic
Ellipsometry (SE), have been proposed. However, in situ SE is associated with
complex hardware and, hence, is resource intensive. To address these
challenges, we propose an ML-based approach to expedite film thickness
estimation. The proposed approach has tremendous implications of faster data
acquisition, reduced hardware complexity and easier integration of
spectroscopic ellipsometry for in situ monitoring of film thickness deposition.
Our experimental results involving SE of TiO2 demonstrate that the proposed
ML-based approach furnishes promising thickness prediction accuracy results of
88.76% within +/-1.5 nm and 85.14% within +/-0.5 nm intervals. Furthermore, we
furnish accuracy results up to 98% at lower thicknesses, which is a significant
improvement over existing SE-based analysis, thereby making our solution a
viable option for thickness estimation of ultrathin films.
