Subwavelength Imaging using a Solid-Immersion Diffractive Optical Processor
arXiv:2401.08923 · doi:10.1186/s43593-024-00067-5
Abstract
Phase imaging is widely used in biomedical imaging, sensing, and material characterization, among other fields. However, direct imaging of phase objects with subwavelength resolution remains a challenge. Here, we demonstrate subwavelength imaging of phase and amplitude objects based on all-optical diffractive encoding and decoding. To resolve subwavelength features of an object, the diffractive imager uses a thin, high-index solid-immersion layer to transmit high-frequency information of the object to a spatially-optimized diffractive encoder, which converts/encodes high-frequency information of the input into low-frequency spatial modes for transmission through air. The subsequent diffractive decoder layers (in air) are jointly designed with the encoder using deep-learning-based optimization, and communicate with the encoder layer to create magnified images of input objects at its output, revealing subwavelength features that would otherwise be washed away due to diffraction limit. We demonstrate that this all-optical collaboration between a diffractive solid-immersion encoder and the following decoder layers in air can resolve subwavelength phase and amplitude features of input objects in a highly compact design. To experimentally demonstrate its proof-of-concept, we used terahertz radiation and developed a fabrication method for creating monolithic multi-layer diffractive processors. Through these monolithically fabricated diffractive encoder-decoder pairs, we demonstrated phase-to-intensity transformations and all-optically reconstructed subwavelength phase features of input objects by directly transforming them into magnified intensity features at the output. This solid-immersion-based diffractive imager, with its compact and cost-effective design, can find wide-ranging applications in bioimaging, endoscopy, sensing and materials characterization.
arXiv comment32 Pages, 9 Figures
References in corpus (12)
- All-Optical Machine Learning Using Diffractive Deep Neural Networks
- Wide-field, high-resolution Fourier ptychographic microscopy
- Nanoscale optical positioning of single quantum dots for bright and pure single-photon emission
- Analysis of Diffractive Optical Neural Networks and Their Integration with Electronic Neural Networks
- Structured illumination microscopy image reconstruction algorithm
- Misalignment Resilient Diffractive Optical Networks
- Combined Multi-Plane Tomographic Phase Retrieval and Stochastic Optical Fluctuation Imaging for 4D Cell Microscopy
- Structured illumination multimodal 3D-resolved quantitative phase and fluorescence sub-diffraction microscopy
- Subwavelength terahertz imaging via virtual superlensing in the radiating near field
- All-optical complex field imaging using diffractive processors
- Non-invasive single-shot recovery of point-spread function of a memory effect based scattering imaging system
- Multispectral Quantitative Phase Imaging Using a Diffractive Optical Network