Diffractive Deep Neural Networks at Visible Wavelengths

Optical deep learning based on diffractive optical elements offers unique advantages for parallel processing,computational speed,and power efficiency.One landmark method is the diffractive deep neural network(D^(2) NN)based on three-dimensional printing technology operated in the terahertz spectral range.Since the terahertz bandwidth involves limited interparticle coupling and material losses,this paper extends D^(2) NN to visible wavelengths.A general theory including a revised formula is proposed to solve any contradictions between wavelength,neuron size,and fabrication limitations.A novel visible light D^(2) NN classifier is used to recognize unchanged targets(handwritten digits ranging from 0 to 9)and targets that have been changed(i.e.,targets that have been covered or altered)at a visible wavelength of 632.8 nm.The obtained experimental classification accuracy(84%)and numerical classification accuracy(91.57%)quantify the match between the theoretical design and fabricated system performance.The presented framework can be used to apply a D^(2) NN to various practical applications and design other new applications.

Pluggable multitask diffractive neural networks based on cascaded metasurfaces

Optical neural networks have significant advantages in terms of power consumption,parallelism,and high computing speed,which has intrigued extensive attention in both academic and engineering communities.It has been considered as one of the powerful tools in promoting the fields of imaging processing and object recognition.However,the existing optical system architecture cannot be reconstructed to the realization of multi-functional artificial intelligence systems simultaneously.To push the development of this issue,we propose the pluggable diffractive neural networks(P-DNN),a general paradigm resorting to the cascaded metasurfaces,which can be applied to recognize various tasks by switching internal plug-ins.As the proof-of-principle,the recognition functions of six types of handwritten digits and six types of fashions are numerical simulated and experimental demonstrated at near-infrared regimes.Encouragingly,the proposed paradigm not only improves the flexibility of the optical neural networks but paves the new route for achieving high-speed,low-power and versatile artificial intelligence systems.

Diffraction deep neural network-based classification for vector vortex beams

The vector vortex beam(VVB)has attracted significant attention due to its intrinsic diversity of information and has found great applications in both classical and quantum communications.However,a VVB is unavoidably affected by atmospheric turbulence(AT)when it propagates through the free-space optical communication environment,which results in detection errors at the receiver.In this paper,we propose a VVB classification scheme to detect VVBs with continuously changing polarization states under AT,where a diffractive deep neural network(DDNN)is designed and trained to classify the intensity distribution of the input distorted VVBs,and the horizontal direction of polarization of the input distorted beam is adopted as the feature for the classification through the DDNN.The numerical simulations and experimental results demonstrate that the proposed scheme has high accuracy in classification tasks.The energy distribution percentage remains above 95%from weak to medium AT,and the classification accuracy can remain above 95%for various strengths of turbulence.It has a faster convergence and better accuracy than that based on a convolutional neural network.

Diffraction deep neural network based orbital angular momentum mode recognition scheme in oceanic turbulence

Orbital angular momentum(OAM)has the characteristics of mutual orthogonality between modes,and has been applied to underwater wireless optical communication(UWOC)systems to increase the channel capacity.In this work,we propose a diffractive deep neural network(DDNN)based OAM mode recognition scheme,where the DDNN is trained to capture the features of the intensity distribution of the OAM modes and output the corresponding azimuthal indices and radial indices.The results show that the proposed scheme can recognize the azimuthal indices and radial indices of the OAM modes accurately and quickly.In addition,the proposed scheme can resist weak oceanic turbulence(OT),and exhibit excellent ability to recognize OAM modes in a strong OT environment.The DDNN-based OAM mode recognition scheme has potential applications in UWOC systems.

Advanced all-optical classification using orbitalangular-momentum-encoded diffractive networks

As a successful case of combining deep learning with photonics,the research on optical machine learning has recently undergone rapid development.Among various optical classification frameworks,diffractive networks have been shown to have unique advantages in all-optical reasoning.As an important property of light,the orbital angular momentum(OAM)of light shows orthogonality and mode-infinity,which can enhance the ability of parallel classification in information processing.However,there have been few all-optical diffractive networks under the OAM mode encoding.Here,we report a strategy of OAM-encoded diffractive deep neural network(OAM-encoded D2NN)that encodes the spatial information of objects into the OAM spectrum of the diffracted light to perform all-optical object classification.We demonstrated three different OAM-encoded D2NNs to realize(1)single detector OAM-encoded D2NN for single task classification,(2)single detector OAM-encoded D2NN for multitask classification,and(3)multidetector OAM-encoded D2NN for repeatable multitask classification.We provide a feasible way to improve the performance of all-optical object classification and open up promising research directions for D2NN by proposing OAMencoded D2NN.

Class-specific differential detection in diffractive optical neural networks improves inference accuracy

Optical computing provides unique opportunities in terms of parallelization,scalability,power efficiency,and computational speed and has attracted major interest for machine learning.Diffractive deep neural networks have been introduced earlier as an optical machine learning framework that uses task-specific diffractive surfaces designed by deep learning to all-optically perform inference,achieving promising performance for object classification and imaging.We demonstrate systematic improvements in diffractive optical neural networks,based on a differential measurement technique that mitigates the strict nonnegativity constraint of light intensity.In this differential detection scheme,each class is assigned to a separate pair of detectors,behind a diffractive optical network,and the class inference is made by maximizing the normalized signal difference between the photodetector pairs.Using this differential detection scheme,involving 10 photodetector pairs behind 5 diffractive layers with a total of 0.2 million neurons,we numerically achieved blind testing accuracies of 98.54%,90.54%,and 48.51%for MNIST,Fashion-MNIST,and grayscale CIFAR-10 datasets,respectively.Moreover,by utilizing the inherent parallelization capability of optical systems,we reduced the cross-talk and optical signal coupling between the positive and negative detectors of each class by dividing the optical path into two jointly trained diffractive neural networks that work in parallel.We further made use of this parallelization approach and divided individual classes in a target dataset among multiple jointly trained diffractive neural networks.Using this class-specific differential detection in jointly optimized diffractive neural networks that operate in parallel,our simulations achieved blind testing accuracies of 98.52%,91.48%,and 50.82%for MNIST,Fashion-MNIST,and grayscale CIFAR-10 datasets,respectively,coming close to the performance of some of the earlier generations of all-electronic deep neural networks,e.g.,LeNet,which achieves classification accuracies of 98.77%,90.27%,and 55.21%corresponding to the same datasets,respectively.In addition to these jointly optimized diffractive neural networks,we also independently optimized multiple diffractive networks and utilized them in a way that is similar to ensemble methods practiced in machine learning;using 3 independently optimized differential diffractive neural networks that optically project their light onto a common output/detector plane,we numerically achieved blind testing accuracies of 98.59%,91.06%,and 51.44%for MNIST,Fashion-MNIST,and grayscale CIFAR-10 datasets,respectively.Through these systematic advances in designing diffractive neural networks,the reported classification accuracies set the state of the art for all-optical neural network design.The presented framework might be useful to bring optical neural network-based low power solutions for various machine learning applications and help us design new computational cameras that are task-specific.

Quantitative phase imaging(QPI)through random diffusers using a diffractive optical network

Quantitative phase imaging(QPI)is a label-free computational imaging technique used in various fields,including biology and medical research.Modern QPI systems typically rely on digital processing using iterative algorithms for phase retrieval and image reconstruction.Here,we report a diffractive optical network trained to convert the phase information of input objects positioned behind random diffusers into intensity variations at the output plane,all-optically performing phase recovery and quantitative imaging of phase objects completely hidden by unknown,random phase diffusers.This QPI diffractive network is composed of successive diffractive layers,axially spanning in total~70λ,where is the illumination wavelength;unlike existing digital image reconstruction and phase retrieval methods,it forms an all-optical processor that does not require external power beyond the illumination beam to complete its QPI reconstruction at the speed of light propagation.This all-optical diffractive processor can provide a low-power,high frame rate and compact alternative for quantitative imaging of phase objects through random,unknown diffusers and can operate at different parts of the electromagnetic spectrum for various applications in biomedical imaging and sensing.The presented QPI diffractive designs can be integrated onto the active area of standard CCD/CMOS-based image sensors to convert an existing optical microscope into a diffractive QPI microscope,performing phase recovery and image reconstruction on a chip through light diffraction within passive structured layers.