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| 1 | Design of task-specific optical systems using broadband diffractive neural networks显示文摘Deep learning has been transformative in many fields,motivating the emergence of various optical computing architectures.Diffractive optical network is a recently introduced optical computing framework that merges wave optics with deep-learning methods to design optical neural networks.Diffraction-based all-optical object recognition systems,designed through this framework and fabricated by 3D printing,have been reported to recognize handwritten digits and fashion products,demonstrating all-optical inference and generalization to sub-classes of data.These previous diffractive approaches employed monochromatic coherent light as the illumination source.Here,we report a broadband diffractive optical neural network design that simultaneously processes a continuum of wavelengths generated by a temporally incoherent broadband source to all-optically perform a specific task learned using deep learning.We experimentally validated the success of this broadband diffractive neural network architecture by designing,fabricating and testing seven different multi-layer,diffractive optical systems that transform the optical wavefront generated by a broadband THz pulse to realize(1)a series of tuneable,single-passband and dual-passband spectral filters and(2)spatially controlled wavelength de-multiplexing.Merging the native or engineered dispersion of various material systems with a deep-learning-based design strategy,broadband diffractive neural networks help us engineer the light–matter interaction in 3D,diverging from intuitive and analytical design methods to create taskspecific optical components that can all-optically perform deterministic tasks or statistical inference for optical machine learning. | Yi Luo Deniz Mengu Nezih T.Yardimci Yair Rivenson Muhammed Veli Mona Jarrahi Aydogan Ozcan | 2019 | Light(Science & Applications)2019，8，1： | 9 |
| 2 | Class-specific differential detection in diffractive optical neural networks improves inference accuracy显示文摘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. | Jingxi Li Deniz Mengu Yi Luo Yair Rivenson Aydogan Ozcan | 2019 | Advanced Photonics2019，1，4： | 6 |
| 3 | To image,or not to image:class-specific diffractive cameras with all-optical erasure of undesired objects显示文摘Privacy protection is a growing concern in the digital era,with machine vision techniques widely used throughout public and private settings.Existing methods address this growing problem by,e.g.,encrypting camera images or obscuring/blurring the imaged information through digital algorithms.Here,we demonstrate a camera design that performs class-specific imaging of target objects with instantaneous all-optical erasure of other classes of objects.This diffractive camera consists of transmissive surfaces structured using deep learning to perform selective imaging of target classes of objects positioned at its input field-of-view.After their fabrication,the thin diffractive layers collectively perform optical mode filtering to accurately form images of the objects that belong to a target data class or group of classes,while instantaneously erasing objects of the other data classes at the output field-of-view.Using the same framework,we also demonstrate the design of class-specific permutation and class-specific linear transformation cameras,where the objects of a target data class are pixel-wise permuted or linearly transformed following an arbitrarily selected transformation matrix for all-optical class-specific encryption,while the other classes of objects are irreversibly erased from the output image.The success of class-specific diffractive cameras was experimentally demonstrated using terahertz(THz)waves and 3D-printed diffractive layers that selectively imaged only one class of the MNIST handwritten digit dataset,all-optically erasing the other handwritten digits.This diffractive camera design can be scaled to different parts of the electromagnetic spectrum,including,e.g.,the visible and infrared wavelengths,to provide transformative opportunities for privacy-preserving digital cameras and task-specific data-efficient imaging. | Bijie Bai Yi Luo Tianyi Gan Jingtian Hu Yuhang Li Yifan Zhao Deniz Mengu Mona Jarrahi Aydogan Ozcan | 2022 | eLight2022，2，1： | 4 |
| 4 | All-optical synthesis of an arbitrary linear transformation using diffractive surfaces显示文摘Spatially-engineered diffractive surfaces have emerged as a powerful framework to control light-matter interactions for statistical inference and the design of task-specific optical components.Here,we report the design of diffractive surfaces to all-optically perform arbitrary complex-valued linear transformations between an input(Ni)and output(No),where Ni and No represent the number of pixels at the input and output fields-of-view(FOVs),respectively.First,we consider a single diffractive surface and use a matrix pseudoinverse-based method to determine the complex-valued transmission coefficients of the diffractive features/neurons to all-optically perform a desired/target linear transformation.In addition to this data-free design approach,we also consider a deep learning-based design method to optimize the transmission coefficients of diffractive surfaces by using examples of input/output fields corresponding to the target transformation.We compared the all-optical transformation errors and diffraction efficiencies achieved using data-free designs as well as data-driven(deep learning-based)diffractive designs to all-optically perform(i)arbitrarily-chosen complex-valued transformations including unitary,nonunitary,and noninvertible transforms,(ii)2D discrete Fourier transformation,(iii)arbitrary 2D permutation operations,and(iv)high-pass filtered coherent imaging.Our analyses reveal that if the total number(N)of spatially-engineered diffractive features/neurons is≥Ni×No,both design methods succeed in all-optical implementation of the target transformation,achieving negligible error.However,compared to data-free designs,deep learning-based diffractive designs are found to achieve significantly larger diffraction efficiencies for a given N and their all-optical transformations are more accurate for N | Onur Kulce Deniz Mengu Yair Rivenson Aydogan Ozcan | 2021 | Light(Science & Applications)2021，10，10： | 3 |
| 5 | Polarization multiplexed diffractive computing:all-optical implementation of a group of linear transformations through a polarization-encoded diffractive network显示文摘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 diffferent 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)N_(i)N_(o),where Ni 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. | Jingxi Li Yi-Chun Hung Onur Kulce Deniz Mengu Aydogan Ozcan | 2022 | Light(Science & Applications)2022，11，7： | 3 |
| 6 | All-optical information-processing capacity of diffractive surfaces显示文摘The precise engineering of materials and surfaces has been at the heart of some of the recent advances in optics and photonics.These advances related to 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 interactions 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.,plasmomc and/or dielectric-based metasurfaces and flat optics,which can be used to form all-optical processors. | Onur Kulce Deniz Mengu Yair Rivenson Aydogan Ozcan | 2021 | Light(Science & Applications)2021，10，2： | 3 |
| 7 | Ensemble learning of diffractive optical networks显示文摘A plethora of research advances have emerged in the fields of optics and photonics that benefit from harnessing the power of machine learning.Specifically,there has been a revival of interest in optical computing hardware due to its potential advantages for machine learning tasks in terms of parallelization,power efficiency and computation speed.Diffractive deep neural networks(D^(2)NNs)form such an optical computing framework that benefits from deep learning-based design of successive diffractive layers to all-optically process information as the input light diffracts through these passive layers.D^(2)NNs have demonstrated success in various tasks,including object classification,the spectral encoding of information,optical pulse shaping and imaging.Here,we substantially improve the inference performance of diffractive optical networks using feature engineering and ensemble learning.After independently training 1252 D^(2)NNs that were diversely engineered with a variety of passive input filters,we applied a pruning algorithm to select an optimized ensemble of D^(2)NNs that collectively improved the image classification accuracy.Through this pruning,we numerically demonstrated that ensembles of N=14 and N=30 D^(2)NNs achieve blind testing accuracies of 61.14±0.23%and 62.13±0.05%,respectively,on the classification of GFAR-10 test images,providing an inference improvennent of>16%compared to the average performance of the individual D^(2)NNs within each ensemble.These results constitute the highest inference accuracies achieved to date by any diffractive optical neural network design on the same dataset and might provide a significant leap to extend the application space of diffractive optical image classification and machine vision systems. | Md Sadman Sakib Rahman Jingxi Li Deniz Mengu Yair Rivenson Aydogan Ozcan | 2021 | Light(Science & Applications)2021，10，1： | 1 |
| 8 | Pediatric dysphagia overview:best practice recommendation study by multidisciplinary experts显示文摘Background Currently,there is no comprehensive and multidisciplinary recommendation study covering all aspects of pediatric dysphagia(PD).This study aimed to generate PD management recommendations with methods that can be used in clinical practice to fill this gap in our country and in the world,from the perspective of experienced multidisciplinary experts.Methods This recommendation paper was generated by a multidisciplinary team,using the seven-step process and a three-round modified Delphi survey via e-mail.First,ten open-ended questions were created,and then detailed recommendations including management,diagnosis,treatment,and follow-up were created with the answers from these questions.Each recommendation item was voted on by the experts as overall consensus(strong recommendation),approaching consensus(weak recommendation)and divergent consensus(not recommended).Results In the 1st Delphi round,a questionnaire of 414 items was prepared based on the experts’responses to ten open-ended questions.In the 2nd Delphi round,59.2%of these items were accepted as pre-recommendation.In the 3rd Delphi round,62.6%of 246 items were accepted for inclusion in the proposals.The final version recommendations consisted of 154 items.Conclusions This study includes comprehensive and detailed answers for every problem that could be posed in clinical practice for the management of PD,and recommendations are for all pediatric patients with both oropharyngeal and esophageal dysphagia. | Ebru Umay Sibel Eyigor Esra Giray Evrim Karadag Saygi Bulent Karadag Nihal Durmus Kocaaslan Deniz Yuksel Arzu Meltem Demir Engin Tutar Canan Tikiz Eda Gurcay Zeliha Unlu Pelin Celik Ece Unlu Akyuz Guven Mengu Serkan Bengisu Sibel Alicura Necati Unver Nida Yekteusaklari Cuma Uz Merve Cikili Uytuni Fatih Bagcier Elif Tarihci Mazlum Serdar Akaltun Iclal Ayranci Sucakli Damla Cankurtaran Zeynep Aykin Resa Aydin Fatma Nazli | 2022 | World Journal of Pediatrics2022，18，11： | 1 |
| 9 | All-optical image denoising using a diffractive visual processor显示文摘Image denoising,one of the essential inverse problems,targets to remove noise/artifacts from input images.In general,digital image denoising algorithms,executed on computers,present latency due to several iterations implemented in,e.g.,graphics processing units(GPUs).While deep learning-enabled methods can operate non-iteratively,they also introduce latency and impose a significant computational burden,leading to increased power consumption.Here,we introduce an analog diffractive image denoiser to all-optically and non-iteratively clean various forms of noise and artifacts from input images–implemented at the speed of light propagation within a thin diffractive visual processor that axially spans<250×λ,whereλis the wavelength of light.This all-optical image denoiser comprises passive transmissive layers optimized using deep learning to physically scatter the optical modes that represent various noise features,causing them to miss the output image Field-of-View(FoV)while retaining the object features of interest.Our results show that these diffractive denoisers can efficiently remove salt and pepper noise and image rendering-related spatial artifacts from input phase or intensity images while achieving an output power efficiency of~30–40%.We experimentally demonstrated the effectiveness of this analog denoiser architecture using a 3D-printed diffractive visual processor operating at the terahertz spectrum.Owing to their speed,power-efficiency,and minimal computational overhead,all-optical diffractive denoisers can be transformative for various image display and projection systems,including,e.g.,holographic displays. | Çağatay Işıl Tianyi Gan Fazil Onuralp Ardic Koray Mentesoglu Jagrit Digani Huseyin Karaca Hanlong Chen Jingxi Li Deniz Mengu Mona Jarrahi Kaan Akşit Aydogan Ozcan | 2024 | Light(Science & Applications)2024，13，3： | 0 |
| 10 | Snapshot multispectral imaging using a diffractive optical network显示文摘Multispectral imaging has been used for numerous applications in e.g.,environmental monitoring,aerospace,defense,and biomedicine.Here,we present a diffractive optical network-based multispectral imaging system trained using deep learning to create a virtual spectral filter array at the output image field-of-view.This diffractive multispectral imager performs spatially-coherent imaging over a large spectrum,and at the same time,routes a pre-determined set of spectral channels onto an array of pixels at the output plane,converting a monochrome focal-plane array or image sensor into a multispectral imaging device without any spectral filters or image recovery algorithms.Furthermore,the spectral responsivity of this diffractive multispectral imager is not sensitive to input polarization states.Through numerical simulations,we present different diffractive network designs that achieve snapshot multispectral imaging with 4,9 and 16 unique spectral bands within the visible spectrum,based on passive spatially-structured diffractive surfaces,with a compact design that axially spans ~72λ_(m),where λ_(m) is the mean wavelength of the spectral band of interest.Moreover,we experimentally demonstrate a diffractive multispectral imager based on a 3D-printed diffractive network that creates at its output image plane a spatially repeating virtual spectral filter array with 2×2=4 unique bands at terahertz spectrum.Due to their compact form factor and computation-free,power-efficient and polarization-insensitive forward operation,diffractive multispectral imagers can be transformative for various imaging and sensing applications and be used at different parts of the electromagnetic spectrum where high-density and wide-area multispectral pixel arrays are not widely available. | Deniz Mengu Anika Tabassum Mona Jarrahi Aydogan Ozcan | 2023 | Light(Science & Applications)2023，12，5： | 0 |
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