Machine-learning-assisted diffractive imaging with subwavelength resolution

Abstract

We develop an approach that enables characterization of wavelength-scale objects with deep subwavelength resolution. The technique combines diffractive imaging that out-couples the information about the subwavelength features of the object into the far-field zone with machine learning that analyzes the resulting patterns. Recovery of complex objects with 120-nm resolution with ~530-nm light is demonstrated experimentally. Our theoretical analysis suggests that the same objects can be recovered with up to 2-micron-wavelength light. Our work opens the door for new characterization tools that combine high spatial resolution, fast data acquisition, and artificial intelligence