Machine Learning-based Diffractive Imaging with Subwavelength Resolution

Abstract

Far-field characterization of small objects is severely constrained by the diffraction limit. Existing tools achieving sub-diffraction resolution often utilize point-by-point image reconstruction via scanning or labelling. Here, we present a new imaging technique capable of fast and accurate characterization of two-dimensional structures with at least /25 resolution, based on a single far-field intensity measurement. Experimentally, we realized this technique resolving the smallest-available to us 180nm-scale features with 532-nm laser light. A comprehensive analysis of machine learning algorithms was performed to gain insight into the learning process and to understand the flow of subwavelength information through the system. Image parameterization, suitable for diffractive configurations and highly tolerant to random noise was developed. The proposed technique can be applied to new characterization tools with high spatial resolution, fast data acquisition, and artificial intelligence, such as high-speed nanoscale metrology and quality control, and can be further developed to highresolution spectroscopy.

Super-Resolution without Imaging: Library-Based Approaches Using Near-to-Far-Field Transduction by a Nanophotonic Structure

“…Finally, we note that the concept of a library, and subsequent retrieval of parameters allows for other solutions than PCA-based techniques. Recently, several groups have addressed related challenges through machine learning, successfully retrieving parameters with subdiffractive precision as good as λ/25 for optical wavelength λ. , For such demonstrations, the number of distinguishable parameter values is typically orders of magnitude smaller than the size of the “training set”, that is, the size of the calibration library that is required. In an additional drawback, it is often difficult to interpret how the neural network performs its function and whether it is robust to minor changes in parameters, regularization being one of the oldest and toughest problems in machine learning.…”