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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 | The intracellular localisation of TAF7L,a paralogue of transcription factor TFIID subunit TAF7,is developmentally regulated during male germcell differentiation显示文摘 | Pointud JC Gabrielle Mengus Stefano Brancorsini | 2003 | Cell Sci2003，116，： | 1 |
| 8 | Elevated levels of circulating IL-7 and IL-15 in patients with early stage prostate cancer显示文摘 | Mengus C Le Magnen C Trella E | 2011 | J Transl Med2011，9，： | 1 |
| 9 | Stressors and job out- comes in sales:a triphasic model versus a linear-quadratic- interactive model显示文摘 | Bhuian SN Mengue B Borsboom R | 2005 | J Bus Res2005，58，： | 1 |
| 10 | Contemporary immunotherapy of solid tumors : From tumor-associated antigens to combination treatment 显示文摘 | Spagnoli GC Ebrahimi M Iezzi G Mengus C Zajac P | 2010 | Curr Opin Drug Discov Devel2010，13，2： | 1 |
| 11 | The implications of socialization and integration in supply chain management 显示文摘 | Cousins P D Mengue B | 2006 | Journal of Operations Management2006，24，5： | 1 |
| 12 | On the FEM modal approach for a reverberation chamber anal- ysis显示文摘 | G'erard Orjubin Elodie Richalot St'ephanie Mengu'e | 2007 | IEEE Transactions on Electromagnetic Compatibility2007，49，1： | 1 |
| 13 | Comparison of the effects of alendronate and risedronate on bone mineral density and bone turnover markers in postmenopausal osteoporosis显示文摘 | Mengu Sarioglu Cigdem Tuzun Zeliha Unlu Canan Tikiz Fatma Taneli B. Sami. Uyanik | 2006 | Rheumatology International2006，，3： | 1 |
| 14 | MAGE-A10 cancer/testis antigen is highly expressed in high-grade non-muscle-invasive bladder carcinomas显示文摘 | Mengus C Schultz-Thater E Coulot J | 2013 | Int J Cancer2013，132，10： | 1 |
| 15 | Computer simulation of the electric field structure and optical emission from cloud-top to the ionosphere 显示文摘 | Cho Mengu Rycroft Michael J | 1998 | Journal of Atmospheric and Solar-Terrestrial Physics(S1364-6826)1998，60，： | 1 |
| 16 | Comparison between different criteria for evaluating reverberation chamber functioning using a 3-D FDTD algorithm显示文摘 | Mengue S Richalot E Picon O | 2008 | IEEE Trans on Electromagn Compat2008，50，2： | 1 |
| 17 | The Employee-organization Relationship, Organizational Citizenship Behavios, and Superiors Service Quality 显示文摘 | BELL S J MENGUE B | 2002 | Journal of Retailing2002，78，2： | 1 |
| 18 | Waist circumference in the prediction of obesity-related adverse pregnancy outcomes显示文摘 | Wendland EM Duncan BB Mengue SS | 2007 | Cad Saude Publica2007，23，： | 1 |
| 19 | Differentialeffects of the tryptophan metabolite 3 - hydroxyanthranilic acid onthe proliferation of human CD8 + T cells induced by TCR trigge-ring or homeostatic cytokines显示文摘 | Weber WP Feder - Mengus C Chiarugi A | 2006 | Eur J Immunol2006，36，2： | 1 |
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