Machine-learning-assisted material and geometry characterization from Casimir force measurement

TL;DR

This paper demonstrates that machine learning can be used to infer material permittivity and film thickness from Casimir force measurements, leveraging the broadband nature of quantum vacuum fluctuations.

Contribution

It introduces a novel approach combining Casimir force measurements with machine learning to characterize material properties over a broad frequency range.

Findings

Machine learning enables inference of film permittivity from Casimir force data.

The method can determine film thickness and permittivity over a broad frequency spectrum.

Vacuum fluctuations can serve as a broadband electromagnetic source for material characterization.

Abstract

A broadband electromagnetic source is important for scientific and technological applications. Quantum vacuum fluctuations, which manifest most prominently in the Casimir effect, provide a fundamentally broadband electromagnetic source. Here we explore a potential consequence of the broadband nature of quantum vacuum fluctuations, by showing that such fluctuations can enable measurement of material permittivity over a broad frequency range. Specifically, we consider the Casimir force in a parallel-plate geometry, with one plate covered by a nanoscopic thin film. Using a machine learning approach, we show that one can infer both the thickness of the film and its permittivity over a broad frequency range, starting from the dependency of the Casimir forces on the spacing between the two plates. Our work highlights the application potential of using vacuum fluctuations as a naturally-existing broadband electromagnetic source for material characterization, and shows that the inverse problem in Casimir force calculation can be solved with machine learning.