All-Optical Phase Conjugation Using Diffractive Wavefront Processing
arXiv:2311.04473 · doi:10.1038/s41467-024-49304-y
Abstract
Optical phase conjugation (OPC) is a nonlinear technique used for counteracting wavefront distortions, with various applications ranging from imaging to beam focusing. Here, we present the design of a diffractive wavefront processor to approximate all-optical phase conjugation operation for input fields with phase aberrations. Leveraging deep learning, a set of passive diffractive layers was optimized to all-optically process an arbitrary phase-aberrated coherent field from an input aperture, producing an output field with a phase distribution that is the conjugate of the input wave. We experimentally validated the efficacy of this wavefront processor by 3D fabricating diffractive layers trained using deep learning and performing OPC on phase distortions never seen by the diffractive processor during its training. Employing terahertz radiation, our physical diffractive processor successfully performed the OPC task through a shallow spatially-engineered volume that axially spans tens of wavelengths. In addition to this transmissive OPC configuration, we also created a diffractive phase-conjugate mirror by combining deep learning-optimized diffractive layers with a standard mirror. Given its compact, passive and scalable nature, our diffractive wavefront processor can be used for diverse OPC-related applications, e.g., turbidity suppression and aberration correction, and is also adaptable to different parts of the electromagnetic spectrum, especially those where cost-effective wavefront engineering solutions do not exist.
arXiv comment34 Pages, 9 Figures
References in corpus (18)
- All-Optical Machine Learning Using Diffractive Deep Neural Networks
- Design of Task-Specific Optical Systems Using Broadband Diffractive Neural Networks
- Spectrally-Encoded Single-Pixel Machine Vision Using Diffractive Networks
- Online learning of the transfer matrix of dynamic scattering media: wavefront shaping meets multidimensional time series
- Terahertz Pulse Shaping Using Diffractive Surfaces
- Computational Imaging Without a Computer: Seeing Through Random Diffusers at the Speed of Light
- Class-specific Differential Detection in Diffractive Optical Neural Networks Improves Inference Accuracy
- All-Optical Information Processing Capacity of Diffractive Surfaces
- Misalignment Resilient Diffractive Optical Networks
- Snapshot Multispectral Imaging Using a Diffractive Optical Network
- Diffractive all-optical computing for quantitative phase imaging
- Massively Parallel Universal Linear Transformations using a Wavelength-Multiplexed Diffractive Optical Network
- To image, or not to image: Class-specific diffractive cameras with all-optical erasure of undesired objects
- Universal Linear Intensity Transformations Using Spatially-Incoherent Diffractive Processors
- Data class-specific all-optical transformations and encryption
- Unidirectional Imaging using Deep Learning-Designed Materials
- Multispectral Quantitative Phase Imaging Using a Diffractive Optical Network
- Phase conjugation with spatially incoherent light in complex media
Cited by in corpus (6)
- Multiplane Quantitative Phase Imaging Using a Wavelength-Multiplexed Diffractive Optical Processor
- Integration of Programmable Diffraction with Digital Neural Networks
- Broadband Unidirectional Visible Imaging Using Wafer-Scale Nano-Fabrication of Multi-Layer Diffractive Optical Processors
- Structural Vibration Monitoring with Diffractive Optical Processors
- Optimizing Structured Surfaces for Diffractive Waveguides
- BlurryScope enables compact, cost-effective scanning microscopy for HER2 scoring using deep learning on blurry images