All-Optical Information Processing Capacity of Diffractive Surfaces

Abstract

Precise engineering of materials and surfaces has been at the heart of some of the recent advances in optics and photonics. These advances around the engineering of materials with new functionalities have also opened up exciting avenues for designing trainable surfaces that can perform computation and machine learning tasks through light-matter interaction and diffraction. Here, we analyze the information processing capacity of coherent optical networks formed by diffractive surfaces that are trained to perform an all-optical computational task between a given input and output field-of-view. We show that the dimensionality of the all-optical solution space covering the complex-valued transformations between the input and output fields-of-view is linearly proportional to the number of diffractive surfaces within the optical network, up to a limit that is dictated by the extent of the input and output fields-of-view. Deeper diffractive networks that are composed of larger numbers of trainable surfaces can cover a higher dimensional subspace of the complex-valued linear transformations between a larger input field-of-view and a larger output field-of-view, and exhibit depth advantages in terms of their statistical inference, learning and generalization capabilities for different image classification tasks, when compared with a single trainable diffractive surface. These analyses and conclusions are broadly applicable to various forms of diffractive surfaces, including e.g., plasmonic and/or dielectric-based metasurfaces and flat optics that can be used to form all-optical processors.

arXiv comment31 Pages, 6 Figures, 1 Table

References in corpus (19)

• Adam: A Method for Stochastic Optimization
• Deep Learning with Coherent Nanophotonic Circuits
• All-Optical Machine Learning Using Diffractive Deep Neural Networks
• A review of metasurfaces: physics and applications
• Optical Cloaking with Non-Magnetic Metamaterials
• Achieving transparency with plasmonic coatings
• Optical Hyperlens: Far-field imaging beyond the diffraction limit
• Dielectric Optical Cloak
• Neuromorphic Silicon Photonic Networks
• Ultrafast active plasmonics: transmission and control of femtosecond plasmon signals
• 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
• Terahertz Pulse Shaping Using Diffractive Surfaces
• Class-specific Differential Detection in Diffractive Optical Neural Networks Improves Inference Accuracy
• Misalignment Resilient Diffractive Optical Networks
• PCNNA: A Photonic Convolutional Neural Network Accelerator
• Principles of Neuromorphic Photonics

Cited by in corpus (39)

• Spectrally-Encoded Single-Pixel Machine Vision Using Diffractive Networks
• Computational Imaging Without a Computer: Seeing Through Random Diffusers at the Speed of Light
• Ensemble learning of diffractive optical networks
• Polarization Multiplexed Diffractive Computing: All-Optical Implementation of a Group of Linear Transformations Through a Polarization-Encoded Diffractive Network
• Snapshot Multispectral Imaging Using a Diffractive Optical Network
• Diffractive all-optical computing for quantitative phase imaging
• Intelligent Meta-Imagers: From Compressed to Learned Sensing
• 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
• Super-resolution image display using diffractive decoders
• All-optical image denoising using a diffractive visual processor
• Data class-specific all-optical transformations and encryption
• Multiplane Quantitative Phase Imaging Using a Wavelength-Multiplexed Diffractive Optical Processor
• Unidirectional Imaging using Deep Learning-Designed Materials
• Cascadable all-optical NAND gates using diffractive networks
• Universal Polarization Transformations: Spatial programming of polarization scattering matrices using a deep learning-designed diffractive polarization transformer
• Classification and reconstruction of spatially overlapping phase images using diffractive optical networks
• Learning Diffractive Optical Communication Around Arbitrary Opaque Occlusions
• Quantitative phase imaging (QPI) through random diffusers using a diffractive optical network
• Ultrahigh-fidelity spatial mode quantum gates in high-dimensional space by diffractive deep neural networks
• Multispectral Quantitative Phase Imaging Using a Diffractive Optical Network
• Simultaneously sorting overlapping quantum states of light
• Complex-valued universal linear transformations and image encryption using spatially incoherent diffractive networks
• All-Optical Phase Conjugation Using Diffractive Wavefront Processing
• Time-lapse image classification using a diffractive neural network
• Optical information transfer through random unknown diffusers using electronic encoding and diffractive decoding
• Multiplexed all-optical permutation operations using a reconfigurable diffractive optical network
• Programmable metasurfaces for future photonic artificial intelligence
• Analysis of Diffractive Neural Networks for Seeing Through Random Diffusers
• Integration of Programmable Diffraction with Digital Neural Networks
• Universal point spread function engineering for 3D optical information processing
• Polarization and Orbital Angular Momentum Encoded Quantum Toffoli Gate Enabled by Diffractive Neural Networks
• Massively parallel and universal approximation of nonlinear functions using diffractive processors
• Unidirectional imaging with partially coherent light
• Broadband Unidirectional Visible Imaging Using Wafer-Scale Nano-Fabrication of Multi-Layer Diffractive Optical Processors
• Optimizing Structured Surfaces for Diffractive Waveguides
• Computer-free, all-optical reconstruction of holograms using diffractive networks
• Diffractive Interconnects: All-Optical Permutation Operation Using Diffractive Networks
• All-Optical Synthesis of an Arbitrary Linear Transformation Using Diffractive Surfaces