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.

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.

All-optical computing based on convolutional neural networks

The rapid development of information technology has fueled an ever-increasing demand for ultrafast and ultralow-en-ergy-consumption computing.Existing computing instruments are pre-dominantly electronic processors,which use elec-trons as information carriers and possess von Neumann architecture featured by physical separation of storage and pro-cessing.The scaling of computing speed is limited not only by data transfer between memory and processing units,but also by RC delay associated with integrated circuits.Moreover,excessive heating due to Ohmic losses is becoming a severe bottleneck for both speed and power consumption scaling.Using photons as information carriers is a promising alternative.Owing to the weak third-order optical nonlinearity of conventional materials,building integrated photonic com-puting chips under traditional von Neumann architecture has been a challenge.Here,we report a new all-optical comput-ing framework to realize ultrafast and ultralow-energy-consumption all-optical computing based on convolutional neural networks.The device is constructed from cascaded silicon Y-shaped waveguides with side-coupled silicon waveguide segments which we termed“weight modulators”to enable complete phase and amplitude control in each waveguide branch.The generic device concept can be used for equation solving,multifunctional logic operations as well as many other mathematical operations.Multiple computing functions including transcendental equation solvers,multifarious logic gate operators,and half-adders were experimentally demonstrated to validate the all-optical computing performances.The time-of-flight of light through the network structure corresponds to an ultrafast computing time of the order of several picoseconds with an ultralow energy consumption of dozens of femtojoules per bit.Our approach can be further expan-ded to fulfill other complex computing tasks based on non-von Neumann architectures and thus paves a new way for on-chip all-optical computing.

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.

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.

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.

Multimode diffractive optical neural network

On-chip diffractive optical neural networks(DONNs)bring the advantages of parallel processing and low energy consumption.However,an accurate representation of the optical field’s evolution in the structure cannot be provided using the previous diffraction-based analysis method.Moreover,the loss caused by the open boundaries poses challenges to applications.A multimode DONN architecture based on a more precise eigenmode analysis method is proposed.We have constructed a universal library of input,output,and metaline structures utilizing this method,and realized a multimode DONN composed of the structures from the library.On the designed multimode DONNs with only one layer of the metaline,the classification task of an Iris plants dataset is verified with an accuracy of 90%on the blind test dataset,and the performance of the one-bit binary adder task is also validated.Compared to the previous architectures,the multimode DONN exhibits a more compact design and higher energy efficiency.

Physics-informed neural networks for diffraction tomography

We propose a physics-informed neural network(PINN)as the forward model for tomographic reconstructions of biological samples.We demonstrate that by training this network with the Helmholtz equation as a physical loss,we can predict the scattered field accurately.It will be shown that a pretrained network can be fine-tuned for different samples and used for solving the scattering problem much faster than other numerical solutions.We evaluate our methodology with numerical and experimental results.Our PINNs can be generalized for any forward and inverse scattering problem.

Computation at the speed of light:metamaterials for all-optical calculations and neural networks

The explosion in the amount of information that is being processed is prompting the need for new computing systems beyond existing electronic computers.Photonic computing is emerging as an attractive alternative due to performing calculations at the speed of light,the change for massive parallelism,and also extremely low energy consumption.We review the physical implementation of basic optical calculations,such as differentiation and integration,using metamaterials,and introduce the realization of all-optical artificial neural networks.We start with concise introductions of the mathematical principles behind such optical computation methods and present the advantages,current problems that need to be overcome,and the potential future directions in the field.We expect that our review will be useful for both novice and experienced researchers in the field of all-optical computing platforms using metamaterials.

Complex-valued universal linear transformations and image encryption using spatially incoherent diffractive networks

As an optical processor,a diffractive deep neural network(D2NN)utilizes engineered diffractive surfaces designed through machine learning to perform all-optical information processing,completing its tasks at the speed of light propagation through thin optical layers.With sufficient degrees of freedom,D2NNs can perform arbitrary complex-valued linear transformations using spatially coherent light.Similarly,D2NNs can also perform arbitrary linear intensity transformations with spatially incoherent illumination;however,under spatially incoherent light,these transformations are nonnegative,acting on diffraction-limited optical intensity patterns at the input field of view.Here,we expand the use of spatially incoherent D2NNs to complex-valued information processing for executing arbitrary complex-valued linear transformations using spatially incoherent light.Through simulations,we show that as the number of optimized diffractive features increases beyond a threshold dictated by the multiplication of the input and output space-bandwidth products,a spatially incoherent diffractive visual processor can approximate any complex-valued linear transformation and be used for all-optical image encryption using incoherent illumination.The findings are important for the all-optical processing of information under natural light using various forms of diffractive surface-based optical processors.

基于物理信息神经网络的光波衍射问题求解

用物理信息神经网络方法数值求解间断系数光波衍射问题.结果表明:用光滑函数近似间断系数可大幅度提高物理信息神经网络求解精度;用物理信息神经网络求解散射场比直接求解总场效果更好.最后通过数值实验验证理论结果的正确性.

基于超材料的电磁神经网络研究进展

随着人工智能技术的广泛应用,面向智能计算的算力需求呈井喷式增长。目前芯片的快速发展已经逼近其工艺制程的瓶颈,同时功耗也不断增加,因此高速、高能效的智能计算硬件研究是一个重要方向。以光子电路神经网络和全光衍射神经网络为代表的计算架构因其计算快、功耗低等优势而受到广泛关注。该文回顾了光神经网络的代表性工作,通过3维衍射神经网络和光神经网络芯片化发展两条主线进行介绍,同时,针对光学衍射神经网络和光子神经网络芯片面临的瓶颈和挑战,如网络规模和集成度等,分析比较它们的特点、性能和各自的优劣势。其次,考虑到通用化的发展需求,该文进一步讨论神经形态计算硬件的可编程设计,并在各个部分中介绍了一些可编程神经网络的代表性工作。除了光波段的智能神经网络,本文还讨论了微波衍射神经网络的发展和应用,展示了其可编程能力。最后介绍智能神经形态计算的未来方向和发展趋势,及其在无线通信、信息处理和传感方面的潜在应用。

多任务衍射神经网络系统设计与实现

为探索利用衍射神经网络执行多任务图像分类识别的可行性,本文设计并搭建一种衍射神经网络系统。该系统采用空间光调制器(Spatial Light Modulator,SLM)做衍射神经网络的相位及振幅权重的调制以及网络层的光学全连接,并利用CMOS相机实现衍射神经网络中各衍射层输出的光电非线性激活与输出图像识别结果判别。设计的系统模型在MNIST和Fashion-MNIST图像分类识别中正确率达到94.1%和92.1%。最终通过搭建光路系统,光学实验正确率分别为91%和81.7%。所设计的衍射神经网络系统可满足多种图像分类识别应用需求,为衍射网络的设计与构建提供了新的思路。

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.

Differential interference contrast phase edging net:an all-optical learning system for edge detection of phase objects

Edge detection for low-contrast phase objects cannot be performed directly by the spatial difference of intensity distribution.In this work,an all-optical diffractive neural network(DPENet)based on the differential interference contrast principle to detect the edges of phase objects in an all-optical manner is proposed.Edge information is encoded into an interference light field by dual Wollaston prisms without lenses and light-speed processed by the diffractive neural network to obtain the scale-adjustable edges.Simulation results show that DPENet achieves F-scores of 0.9308(MNIST)and 0.9352(NIST)and enables real-time edge detection of biological cells,achieving an F-score of 0.7462.

Polarization and wavelength routers based on difractive neural network

In the feld of information processing,all-optical routers are signifcant for achieving high-speed,high-capacity signal processing and transmission.In this study,we developed three types of structurally simple and fexible routers using the deep difractive neural network(D2 NN),capable of routing incident light based on wavelength and polarization.First,we implemented a polarization router for routing two orthogonally polarized light beams.The second type is the wavelength router that can route light with wavelengths of 1550,1300,and 1100 nm,demonstrating outstanding performance with insertion loss as low as 0.013 dB and an extinction ratio of up to 18.96 dB,while also maintaining excellent polarization preservation.The fnal router is the polarization-wavelength composite router,capable of routing six types of input light formed by pairwise combinations of three wavelengths(1550,1300,and 1100 nm)and two orthogonal linearly polarized lights,thereby enhancing the information processing capability of the device.These devices feature compact structures,maintaining high contrast while exhibiting low loss and passive characteristics,making them suitable for integration into future optical components.This study introduces new avenues and methodologies to enhance performance and broaden the applications of future optical information processing systems.

Integrated diffractive optical neural network with space-time interleaving

Integrated diffractive optical neural networks(DONNs)have significant potential for complex machine learning tasks with high speed and ultralow energy consumption.However,the on-chip implementation of a high-performance optical neural network is limited by input dimensions.In contrast to existing photonic neural networks,a space-time interleaving technology based on arrayed waveguides is designed to realize an on-chip DONN with high-speed,high-dimensional,and all-optical input signal modulation.To demonstrate the performance of the on-chip DONN with high-speed space-time interleaving modulation,an on-chip DONN with a designed footprint of 0.0945 mm~2is proposed to resolve the vowel recognition task,reaching a computation speed of about 1.4×10^(13)operations per second and yielding an accuracy of 98.3%in numerical calculation.In addition,the function of the specially designed arrayed waveguides for realizing parallel signal inputs using space-time conversion has been verified experimentally.This method can realize the on-chip DONN with higher input dimension and lower energy consumption.

基于光学衍射神经网络的拉盖尔-高斯光束识别

拉盖尔–高斯(Laguerre-Gaussian,LG)光束除了轨道角动量(orbital angular momentum,OAM)维度外,还拥有径向量子数p,因此LG光束可以为光通信和光计算等应用提供更多的物理自由度。但目前常见的干涉、衍射机制的LG光束模式探测方法在受到大气湍流的干扰时,识别准确率会明显下降,从而限制了其实际应用。提出了一种基于衍射神经网络(diffractive neural network,DNN)的LG光束识别方式,实现了p在1~3范围内的识别。即使在强湍流强度,衍射距离为5 m的情况下,该识别方式的识别准确率依然能达到95%以上。该DNN方法能够为准确识别LG光束模式提供有效途径,在大容量OAM通信、高维量子信息处理等方面均具有潜在应用价值。

小波变换和神经网络的X射线衍射波谱滤噪技术比较

多晶X射线衍射扫描所形成的波谱会附带一些背景和噪声,而噪声是影响衍射谱峰分辨率的主要因素。本文通过多晶X射线衍射仪扫描某铁矿石矿物组成所形成的图谱,导入到MATLAB,分别采用神经网络、系统仿真和小波分析三种手段,对该图谱进行滤噪。最终对这三种滤噪方式的滤噪效果进行比较,得出小波分析的方法滤噪效果最佳。

三层衍射神经网络实现手写数字识别

光学衍射神经网络(optical diffraction neural network,ODNN)以光波作为计算媒介执行神经网络的逻辑分析与运算功能,具有高速度、低功耗及并行处理的优势.本文设计了一种仅有三层相位调制的ODNN,提出了基于目标空间频率一级谱分布提升ODNN的数字识别性能的方法,经优化获得了系统最优的像素大小、衍射距离,以及最佳的三层相位分布.设计的ODNN对MNIST手写体数字集识别准确率达到了95.3%,高于文献中采用五层衍射神经网络实现的准确率91.75%(Lin X,Rivenson Y,Yardimci N T,Veli M,Luo Y,Jarrahi M,Ozcan A 2018 Science 3611004),且精简了系统结构.结合ODNN高速度、低功耗的优点,提出的基于频谱分析方法有利于提高ODNN的性能,使ODNN在边缘计算领域有巨大的应用潜力.