Polarization Multiplexed Diffractive Computing: All-Optical Implementation of a Group of Linear Transformations Through a Polarization-Encoded Diffractive Network

Abstract

Research on optical computing has recently attracted significant attention due to the transformative advances in machine learning. Among different approaches, diffractive optical networks composed of spatially-engineered transmissive surfaces have been demonstrated for all-optical statistical inference and performing arbitrary linear transformations using passive, free-space optical layers. Here, we introduce a polarization multiplexed diffractive processor to all-optically perform multiple, arbitrarily-selected linear transformations through a single diffractive network trained using deep learning. In this framework, an array of pre-selected linear polarizers is positioned between trainable transmissive diffractive materials that are isotropic, and different target linear transformations (complex-valued) are uniquely assigned to different combinations of input/output polarization states. The transmission layers of this polarization multiplexed diffractive network are trained and optimized via deep learning and error-backpropagation by using thousands of examples of the input/output fields corresponding to each one of the complex-valued linear transformations assigned to different input/output polarization combinations. Our results and analysis reveal that a single diffractive network can successfully approximate and all-optically implement a group of arbitrarily-selected target transformations with a negligible error when the number of trainable diffractive features/neurons (N) approaches N_p x N_i x N_o, where N_i and N_o represent the number of pixels at the input and output fields-of-view, respectively, and N_p refers to the number of unique linear transformations assigned to different input/output polarization combinations. This polarization-multiplexed all-optical diffractive processor can find various applications in optical computing and polarization-based machine vision tasks.

arXiv comment31 pages, 7 figures

References in corpus (31)

• Deep Learning with Coherent Nanophotonic Circuits
• All-Optical Machine Learning Using Diffractive Deep Neural Networks
• Parallel convolution processing using an integrated photonic tensor core
• Photonics for artificial intelligence and neuromorphic computing
• 11 TeraFLOPs per second photonic convolutional accelerator for deep learning optical neural networks
• Universal Linear Optics
• Neuromorphic Silicon Photonic Networks
• Deep physical neural networks enabled by a backpropagation algorithm for arbitrary physical systems
• Large-scale neuromorphic optoelectronic computing with a reconfigurable diffractive processing unit
• All Optical Neural Network with Nonlinear Activation Functions
• Reinforcement Learning in a large scale photonic Recurrent Neural Network
• Wave Physics as an Analog Recurrent Neural Network
• Design of Task-Specific Optical Systems Using Broadband Diffractive Neural Networks
• Analysis of Diffractive Optical Neural Networks and Their Integration with Electronic Neural Networks
• Spectrally-Encoded Single-Pixel Machine Vision Using Diffractive Networks
• Scalable Optical Learning Operator
• Photonic tensor cores for machine learning
• 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
• Ensemble learning of diffractive optical networks
• Optical Reservoir Computing using multiple light scattering for chaotic systems prediction
• Diffractive all-optical computing for quantitative phase imaging
• Scale-, shift- and rotation-invariant diffractive optical networks
• Deep learning-based holographic polarization microscopy
• Photonic crystal polarizers and polarizing beam splitters
• Cascadable all-optical NAND gates using diffractive networks
• Classification and reconstruction of spatially overlapping phase images using diffractive optical networks
• Polarization-multiplexing ghost imaging

Cited by in corpus (18)

• Snapshot Multispectral Imaging Using a Diffractive Optical Network
• Massively Parallel Universal Linear Transformations using a Wavelength-Multiplexed Diffractive Optical Network
• Rapid Sensing of Hidden Objects and Defects using a Single-Pixel Diffractive Terahertz Processor
• All-optical image classification through unknown random diffusers using a single-pixel diffractive network
• Universal Linear Intensity Transformations Using Spatially-Incoherent Diffractive Processors
• Super-resolution image display using diffractive decoders
• All-optical image denoising using a diffractive visual processor
• Unidirectional Imaging using Deep Learning-Designed Materials
• Universal Polarization Transformations: Spatial programming of polarization scattering matrices using a deep learning-designed diffractive polarization transformer
• Bidirectional Vectorial Holography Using Bi-Layer Metasurfaces and Its Application to Optical Encryption
• Multispectral Quantitative Phase Imaging Using a Diffractive Optical Network
• Time-lapse image classification using a diffractive neural network
• Multiplexed all-optical permutation operations using a reconfigurable diffractive optical network
• Optimizing Structured Surfaces for Diffractive Waveguides
• Polarimetric compressed sensing with hollow, self-assembled diffractive films
• Nonvolatile reconfigurable polarization rotator at datacom wavelengths based on a Sb2Se3/Si waveguide
• Polarization based modulation of splitting ratio in femtosecond laser direct written directional couplers
• Snapshot 3D image projection using a diffractive decoder