Diffractive Characterization of Sub-wavelength Objects with Machine Learning

We analyze the limits of a novel machine-learning based technique for characterization of sub-wavelength objects based on their diffractive signatures, achieving theoretical resolution of ~wavelength/25. Experimentally, we demonstrate characterization of 120-nm objects with 850-nm light.