Robust optical picometrology through data diversity

Abstract

Topologically structured light contains deeply subwavelength features, such as phase singularities, and the scattering of such light can therefore be sensitive to the geometry or movement of scattering objects at such scales. Indeed, it has been shown recently that single-shot optical measurements can yield positional precision better than 100 pm (less than one five-thousandth of the wavelength λ) via a deep-learning-enabled analysis of scattering patterns. Measurement performance, and the extent to which it can be sustained, are constrained by the quality and depth of neural network training data and the stability of the experimental apparatus. Here, we show that a neural network can be trained through exposure to an extended envelope of instrumental/ambient noise conditions to robustly quantify picometric displacements of a target against orders-of-magnitude larger background fluctuations, to maintain precision and accuracy of 100–150 pm in optical measurements (at λ = 488 nm) of nanowire positional change. This capability opens up a range of application opportunities, for example in the optical study of nanostructural dynamics, stiction, material fatigue, and phase transitions.