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.

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.

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.

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.

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。

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.

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。

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.

光栅反散射问题的神经网络方法

针对测量的近场数据来研究光栅形状重构问题,提出了一种基于端对端结构的神经网络方法。该方法是一种循环神经网络,采用序列对序列的方式进行计算。网络模型以近场数据作为输入,以光栅形状参数作为输出,先利用编码端对输入的近场数据进行特征提取,再通过Adam算法更新模型权重,最后使用解码端进行光栅形状参数的反演。此外,模型利用多个门控循环单元从近场数据中提取近场特征,并将该特征引入到解码端中,为反演光栅形状参数提供了更多的特征参考,进一步提高反演效果。数值实验说明该方法可以有效地重构光栅的形状。

利用Encoder-Decoder框架的深度学习网络实现绕射波分离及成像

利用单纯绕射波场实现地下地质异常体的识别具有坚实的理论基础,对应的实施方法得到了广泛研究,且有效地应用于实际勘探。但现有技术在微小尺度异常体成像方面收效甚微,相关研究多数以射线传播理论为基础,对于影响绕射波分离成像精度的因素分析并不完备。相较于反射波,由于存在不连续构造而产生的绕射波能量微弱并且相互干涉,同时环境干扰使得绕射波进一步湮没。因此,更高精度的波场分离及单独成像是现阶段基于绕射波超高分辨率处理、解释的重点研究方向。为此,首先针对地球物理勘探中地质异常体的准确定位,以携带高分辨率信息的绕射波为研究对象,系统分析在不同尺度、不同物性参数的异常体情况下绕射波的能量大小及形态特征,掌握绕射波与其他类型波叠加的具体形式;然后根据相应特征性质提出基于深度学习技术的绕射波分离成像方法,即利用Encoder-Decoder框架的空洞卷积网络捕获绕射波场特征,从而实现绕射波分离,基于速度连续性原则构建单纯绕射波场的偏移速度模型并完成最终成像。数据测试表明,该方法最终可满足微小地质异常体高精度识别的需求。

Massively parallel universal linear transformations using a wavelength-multiplexed diffractive optical network

Large-scale linear operations are the cornerstone for performing complex computational tasks.Using optical computing to perform linear transformations offers potential advantages in terms of speed,parallelism,and scalability.Previously,the design of successive spatially engineered diffractive surfaces forming an optical network was demonstrated to perform statistical inference and compute an arbitrary complex-valued linear transformation using narrowband illumination.We report deep-learning-based design of a massively parallel broadband diffractive neural network for all-optically performing a large group of arbitrarily selected,complex-valued linear transformations between an input and output field of view,each with Ni and No pixels,respectively.This broadband diffractive processor is composed of Nw wavelength channels,each of which is uniquely assigned to a distinct target transformation;a large set of arbitrarily selected linear transformations can be individually performed through the same diffractive network at different illumination wavelengths,either simultaneously or sequentially(wavelength scanning).We demonstrate that such a broadband diffractive network,regardless of its material dispersion,can successfully approximate Nw unique complex-valued linear transforms with a negligible error when the number of diffractive neurons(N)in its design is≥2NwNiNo.We further report that the spectral multiplexing capability can be increased by increasing N;our numerical analyses confirm these conclusions for Nw>180 and indicate that it can further increase to Nw∼2000,depending on the upper bound of the approximation error.Massively parallel,wavelength-multiplexed diffractive networks will be useful for designing highthroughput intelligent machine-vision systems and hyperspectral processors that can perform statistical inference and analyze objects/scenes with unique spectral properties.

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.

基于卷积神经网络的衍射图空间群识别研究

X射线衍射(XRD)图谱数据的采集和分析是新材料开发周期中必不可少的步骤之一,常规实验表征很难实现大批量的测试和快速鉴别.文章基于DenseNet设计了一个衍射图空间群识别的神经网络模型SE-DenseNet.SE-Dense Net在简化了网络结构的同时,通过增加注意力机制(Squeeze and Excitation,SE),并采用新的激活函数来提高网络模型的性能.研究表明,在具有32337个样本包含20类空间群的数据集上,SE-Dense Net的准确率为81.73%,较基础对照模型提高了4.9%.研究发现,尽管数据集的不平衡性是限制神经网络模型预测准确度的主要原因之一,但SE-DenseNet的性能足以在短时间对大量衍射图数据产生准确的预测,并提供有意义的参考.

深度学习点衍射干涉三维坐标定位技术

为了提高现有的三维坐标定位技术的测量精度、稳定性和测量效率,提出了基于深度学习的点衍射干涉三维坐标定位方法。该方法设计了一个深度神经网络用于点衍射干涉场的坐标重构,将相位差矩阵作为输入,构建训练数据集,将点衍射源坐标作为输出,训练神经网络模型。利用训练有素的神经网络对测量到的相位分布进行初步处理,将相位信息转换为点衍射源坐标,根据得到的点衍射源坐标进一步修改粒子群算法的初始粒子,进而重构出高精度的三维坐标值。该神经网络为建立干涉场相位分布与点衍射源坐标之间的非线性关系提供了一种可行的方法,显著提高了三维坐标定位的精度、稳定性和测量效率。为验证所提方法的可行性,进行了数值仿真和实验验证,采用不同的方法进行反复对比与分析。结果表明:所提方法的单次测量时间均在0.05 s左右,其实验精度能够达到亚微米量级,重复性实验的均值和RMS值分别为0.05μm和0.05μm,充分证明了该方法的可行性,并证明了其良好的测量精度和可重复性,为三维坐标定位提供了一种有效可行的方法。

用于宽带光谱滤波的光学衍射神经网络的设计

光谱处理在光学研究和应用中具有重要意义。针对特定任务,已经开发了各种设备和仪器进行光谱的滤波、整形与分析、波长解复用等,但还没有具有先进处理能力的多任务光谱处理设备。设计了一种用于光谱滤波的衍射神经网络,其由相位调制型衍射层与探测层构成。在训练过程中加入了波长参数,以实现对宽带信号的处理;通过损失函数的设计,可以对输出光谱进行控制。以可见光波段的宽带信号为例,实现了单、双通带光谱滤波,且中心波段的宽度和相对强度可调节。证明了该光学衍射神经网络可以有效处理宽带光谱,并为实现更复杂的光谱处理任务奠定了基础。