Article
Nanoscience & Nanotechnology
Deniz Mengu, Yifan Zhao, Anika Tabassum, Mona Jarrahi, Aydogan Ozcan
Summary: This study presents the use of diffractive optical networks to perform permutation operations with a large number of input-output connections. By using deep learning techniques, the capacity of the diffractive optical network to approximate permutation operations increases with the number of diffractive layers and trainable transmission elements in the system. The authors also addressed challenges related to physical alignment and output efficiency by designing misalignment tolerant diffractive designs. The proposed diffractive permutation networks have potential applications in security, image encryption, and data processing, and can serve as channel routing and interconnection panels in wireless networks.
Article
Nanoscience & Nanotechnology
Deniz Mengu, Yair Rivenson, Aydogan Ozcan
Summary: The recent research efforts in optical computing have shifted towards developing optical neural networks that take advantage of the processing speed and parallelism of optics/photonics in machine learning applications. Diffractive Deep Neural Networks (D(2)NNs) utilize light-matter interaction over trainable surfaces, designed using deep learning, to perform statistical inference tasks as light waves propagate through them. The new training strategy introduces input object transformations as uniformly distributed random variables to improve the network's resilience against such transformations, leading to scale-, shift-, and rotation-invariant designs that are crucial for dynamic machine vision applications.
Article
Engineering, Multidisciplinary
Hang Chen, Jianan Feng, Minwei Jiang, Yiqun Wang, Jie Lin, Jiubin Tan, Peng Jin
Summary: This study extends the diffractive deep neural network (DNN)-N-2 to visible wavelengths and proposes a general theory to solve contradictions between wavelength, neuron size, and fabrication limitations. The novel visible light (DNN)-N-2 classifier successfully recognizes handwritten digits and altered targets.
Article
Automation & Control Systems
Md Sadman Sakib Rahman, Aydogan Ozcan
Summary: This study demonstrates for the first time a time-lapse image classification scheme using a diffractive network, improving classification accuracy and generalization performance by leveraging the lateral movements of the input objects and/or the diffractive network. Numerical exploration reveals a blind testing accuracy of 62.03% on the optical classification of objects from the CIFAR-10 dataset using time-lapse diffractive networks, achieving the highest inference accuracy so far. Time-lapse diffractive networks will be widely beneficial for spatiotemporal analysis of input signals using all-optical processors.
ADVANCED INTELLIGENT SYSTEMS
(2023)
Article
Nanoscience & Nanotechnology
Zhengyang Duan, Hang Chen, Xing Lin
Summary: Photonic neural networks utilize photons instead of electrons to perform AI tasks, but current architectures are not capable of multiplexing different tasks in parallel. This paper proposes an optical multitask learning system using multiwavelength diffractive deep neural networks (D(2)NNs) with joint optimization. By encoding multitask inputs into multiwavelength channels, the system achieves high accuracy in parallel multitask performance. Numerical evaluations show that multiwavelength D(2)NNs achieve significantly higher classification accuracies for multitask learning compared to single-wavelength D(2)NNs. This work opens new possibilities for high-throughput neuromorphic photonic computing and parallel execution of multiple tasks in AI systems.
Article
Optics
Md Sadman Sakib Rahman, Jingxi Li, Deniz Mengu, Yair Rivenson, Aydogan Ozcan
Summary: Researchers in the USA have made significant improvements in the performance of diffractive optical networks, signaling a major advancement in their use for optics-based computation and machine learning. By utilizing feature engineering and ensemble learning, they were able to substantially enhance the statistical inference capabilities of these networks.
LIGHT-SCIENCE & APPLICATIONS
(2021)
Article
Engineering, Electrical & Electronic
Yuhang Li, Yi Luo, Bijie Bai, Aydogan Ozcan
Summary: Imaging through diffusive media is a challenging problem that typically requires digital computers for image reconstruction. In this study, we propose an alternative method using diffractive neural networks to see through random, unknown phase diffusers. Through detailed analysis, we observed a trade-off between image reconstruction fidelity and distortion reduction capability of the diffractive network. We also discovered that training the network with a variety of random diffusers and introducing misalignments improved its generalization performance.
IEEE JOURNAL OF SELECTED TOPICS IN QUANTUM ELECTRONICS
(2023)
Article
Optics
Jingxi Li, Tianyi Gan, Bijie Bai, Yi Luo, Mona Jarrahi, Aydogan Ozcan
Summary: Large-scale linear operations are crucial for complex computational tasks, and optical computing offers advantages in terms of speed, parallelism, and scalability. The deep-learning-based design of a broadband diffractive neural network enables the performance of a large group of complex-valued linear transformations. By assigning different illumination wavelengths to each transformation, a single diffractive network can execute multiple linear transformations simultaneously or sequentially. This technology allows for the approximation of unique linear transforms with negligible errors, and the spectral multiplexing capability can be increased by increasing the number of diffractive neurons.
ADVANCED PHOTONICS
(2023)
Article
Chemistry, Multidisciplinary
Yuhang Li, Jingxi Li, Yifan Zhao, Tianyi Gan, Jingtian Hu, Mona Jarrahi, Aydogan Ozcan
Summary: A universal polarization transformer is demonstrated that can synthesize various complex-valued polarization scattering matrices, providing a solution for controlled synthesis of optical fields with nonuniform polarization distributions.
ADVANCED MATERIALS
(2023)
Article
Materials Science, Multidisciplinary
Deniz Mengu, Aydogan Ozcan
Summary: This article demonstrates a diffractive QPI network that can achieve all-optical phase recovery. The quantitative phase image of an object is synthesized by converting the input phase information into intensity variations at the output plane. This network can potentially replace traditional QPI systems and alleviate the computational burden, leading to efficient, high frame-rate, and compact phase imaging systems.
ADVANCED OPTICAL MATERIALS
(2022)
Article
Nanoscience & Nanotechnology
Gordon H. Y. Li, Ryoto Sekine, Rajveer Nehra, Robert M. Gray, Luis Ledezma, Qiushi Guo, Alireza Marandi
Summary: The introduction of energy-efficient hardware accelerators has become necessary in recent years due to the computational demands of deep learning applications. Optical neural networks have shown promise as a potential solution, but their progress has been limited by a lack of energy-efficient nonlinear optical functions. This study demonstrates an all-optical Rectified Linear Unit (ReLU) using a periodically-poled thin-film lithium niobate nanophotonic waveguide, achieving ultra-low energies and near-instantaneous operation. This provides a practical path towards all-optical and energy-efficient nanophotonic deep learning.
Article
Chemistry, Analytical
Yongji Long, Zirong Wang, Bin He, Ting Nie, Xingxiang Zhang, Tianjiao Fu
Summary: By constructing a partitionable adaptive multilayer diffractive optical neural network, the setup issues and the difficulty of flexibly changing the number of layers in multilayer diffractive optical neural network systems are addressed, and high classification performance is achieved.
Article
Chemistry, Multidisciplinary
Xumin Ding, Zihan Zhao, Peng Xie, Dayu Cai, Fanyi Meng, Cong Wang, Qun Wu, Jian Liu, Shah Nawaz Burokur, Guangwei Hu
Summary: This paper presents a novel optical logic operator based on a multifunctional metasurface driven by an all-optical diffractive neural network. It achieves four principal quantum logic operations and demonstrates high fidelities for all four gates.
ADVANCED MATERIALS
(2023)
Review
Optics
Yichen Sun, Mingli Dong, Mingxin Yu, Xiaolin Liu, Lianqing Zhu
Summary: The UCLA research group developed the world's first all-optical diffraction deep neural network system, which can perform classification tasks at near-light-speed. They used a terahertz light source as the input, optimized the model parameters, and utilized 3D printing technology to construct the system. This research opens up new possibilities for optical neural networks.
JOURNAL OF THE OPTICAL SOCIETY OF AMERICA B-OPTICAL PHYSICS
(2023)
Article
Multidisciplinary Sciences
Tao Yan, Rui Yang, Ziyang Zheng, Xing Lin, Hongkai Xiong, Qionghai Dai
Summary: Photonic neural networks use photons instead of electrons to perform brain-like computations, leading to significantly improved computing performance. However, current architectures are limited to handling data with regular structures and cannot generalize to graph-structured data beyond Euclidean space. In this study, a diffractive graph neural network (DGNN) is proposed to address this limitation by utilizing diffractive photonic computing units (DPUs) and on-chip optical devices. DGNN achieves complex feature representation by capturing dependencies among node neighborhoods during light-speed optical message passing over graph structures. It demonstrates superior performance in node and graph-level classification tasks with benchmark databases, providing a new direction for high-efficiency processing of large-scale graph data structures using deep learning.
Article
Nanoscience & Nanotechnology
Deniz Mengu, Yifan Zhao, Anika Tabassum, Mona Jarrahi, Aydogan Ozcan
Summary: This study presents the use of diffractive optical networks to perform permutation operations with a large number of input-output connections. By using deep learning techniques, the capacity of the diffractive optical network to approximate permutation operations increases with the number of diffractive layers and trainable transmission elements in the system. The authors also addressed challenges related to physical alignment and output efficiency by designing misalignment tolerant diffractive designs. The proposed diffractive permutation networks have potential applications in security, image encryption, and data processing, and can serve as channel routing and interconnection panels in wireless networks.
Article
Engineering, Electrical & Electronic
Yuhang Li, Yi Luo, Bijie Bai, Aydogan Ozcan
Summary: Imaging through diffusive media is a challenging problem that typically requires digital computers for image reconstruction. In this study, we propose an alternative method using diffractive neural networks to see through random, unknown phase diffusers. Through detailed analysis, we observed a trade-off between image reconstruction fidelity and distortion reduction capability of the diffractive network. We also discovered that training the network with a variety of random diffusers and introducing misalignments improved its generalization performance.
IEEE JOURNAL OF SELECTED TOPICS IN QUANTUM ELECTRONICS
(2023)
Review
Radiology, Nuclear Medicine & Medical Imaging
Gilbert Hangel, Barbara Schmitz-Abecassis, Nico Sollmann, Joana Pinto, Fatemehsadat Arzanforoosh, Frederik Barkhof, Thomas Booth, Marta Calvo-Imirizaldu, Guilherme Cassia, Marek Chmelik, Patricia Clement, Ece Ercan, Maria A. Fernandez-Seara, Julia Furtner, Elies Fuster-Garcia, Matthew Grech-Sollars, N. Tugay Guven, Gokce Hale Hatay, Golestan Karami, Vera C. Keil, Mina Kim, Johan A. F. Koekkoek, Simran Kukran, Laura Mancini, Ruben Emanuel Nechifor, Alpay Ozcan, Esin Ozturk-Isik, Senol Piskin, Kathleen M. Schmainda, Siri F. Svensson, Chih-Hsien Tseng, Saritha Unnikrishnan, Frans Vos, Esther Warnert, Moss Y. Zhao, Radim Jancalek, Teresa Nunes, Lydiane Hirschler, Marion Smits, Jan Petr, Kyrre E. Emblem
Summary: Preoperative clinical MRI protocols for gliomas still rely on conventional structural MRI, which does not provide information on tumor genotype and has limitations in the delineation of diffuse gliomas. The GliMR COST action aims to raise awareness about advanced MRI techniques in gliomas and their potential clinical translation. This review summarizes current methods, limitations, and applications of advanced MRI for the preoperative assessment of glioma, and evaluates the level of clinical validation of different techniques.
JOURNAL OF MAGNETIC RESONANCE IMAGING
(2023)
Review
Radiology, Nuclear Medicine & Medical Imaging
Lydiane Hirschler, Nico Sollmann, Barbara Schmitz-Abecassis, Joana Pinto, Fatemehsadat Arzanforoosh, Frederik Barkhof, Thomas Booth, Marta Calvo-Imirizaldu, Guilherme Cassia, Marek Chmelik, Patricia Clement, Ece Ercan, Maria A. Fernandez-Seara, Julia Furtner, Elies Fuster-Garcia, Matthew Grech-Sollars, Nazmiye Tugay Guven, Gokce Hale Hatay, Golestan Karami, Vera C. Keil, Mina Kim, Johan A. F. Koekkoek, Simran Kukran, Laura Mancini, Ruben Emanuel Nechifor, Alpay Oezcan, Esin Ozturk-Isik, Senol Piskin, Kathleen Schmainda, Siri F. Svensson, Chih-Hsien Tseng, Saritha Unnikrishnan, Frans Vos, Esther Warnert, Moss Y. Zhao, Radim Jancalek, Teresa Nunes, Kyrre E. Emblem, Marion Smits, Jan Petr, Gilbert Hangel
Summary: This article introduces the preoperative clinical magnetic resonance imaging (MRI) protocols for gliomas, which still rely on conventional structural MRI. However, this method lacks information on tumor genotype and has limitations in delineating diffuse gliomas. The GliMR COST action aims to raise awareness and discuss the clinical translation of advanced MRI techniques for gliomas.
JOURNAL OF MAGNETIC RESONANCE IMAGING
(2023)
Article
Multidisciplinary Sciences
Jingxi Li, Tianyi Gan, Yifan Zhao, Bijie Bai, Che-Yung Shen, Songyu Sun, Mona Jarrahi, Aydogan Ozcan
Summary: The first demonstration of unidirectional imagers is reported, presenting polarization-insensitive and broadband uni-directional imaging based on successive diffractive layers that are linear and isotropic. The diffractive unidirectional imager maintains its functionality over a large spectral band and works under broadband illumination. The diffractive unidirectional imaging using structured materials will have applications in security, defense, telecommunications, and privacy protection.
Article
Engineering, Biomedical
Tairan Liu, Yuzhu Li, Hatice Ceylan Koydemir, Yijie Zhang, Ethan Yang, Merve Eryilmaz, Hongda Wang, Jingxi Li, Bijie Bai, Guangdong Ma, Aydogan Ozcan
Summary: An automated plaque assay leveraging lens-free holographic imaging and deep learning rapidly and accurately detects the cell-lysing events caused by viral replication.
NATURE BIOMEDICAL ENGINEERING
(2023)
Article
Chemistry, Multidisciplinary
Yuhang Li, Jingxi Li, Yifan Zhao, Tianyi Gan, Jingtian Hu, Mona Jarrahi, Aydogan Ozcan
Summary: A universal polarization transformer is demonstrated that can synthesize various complex-valued polarization scattering matrices, providing a solution for controlled synthesis of optical fields with nonuniform polarization distributions.
ADVANCED MATERIALS
(2023)
Article
Engineering, Electrical & Electronic
Hanlong Chen, Luzhe Huang, Tairan Liu, Aydogan Ozcan
Summary: The application of deep learning techniques has significantly improved holographic imaging capabilities, with enhanced phase recovery and image reconstruction. This study introduces eFIN, a deep neural network that utilizes pixel super-resolution and image autofocusing for hologram reconstruction. Experimental results demonstrate the superior image quality and external generalization of eFIN, which achieves a wide autofocusing range and accurately predicts hologram axial distances. The network also enables 3x pixel super-resolution and improves the space-bandwidth product of reconstructed images by 9-fold.
IEEE JOURNAL OF SELECTED TOPICS IN QUANTUM ELECTRONICS
(2023)
Article
Computer Science, Artificial Intelligence
Luzhe Huang, Hanlong Chen, Tairan Liu, Aydogan Ozcan
Summary: GedankenNet is a self-supervised learning model that achieves image reconstruction without the need for labelled or experimental training data, demonstrating superior generalization on hologram reconstruction tasks.
NATURE MACHINE INTELLIGENCE
(2023)
Article
Optics
Jingxi Li, Tianyi Gan, Bijie Bai, Yi Luo, Mona Jarrahi, Aydogan Ozcan
Summary: Large-scale linear operations are crucial for complex computational tasks, and optical computing offers advantages in terms of speed, parallelism, and scalability. The deep-learning-based design of a broadband diffractive neural network enables the performance of a large group of complex-valued linear transformations. By assigning different illumination wavelengths to each transformation, a single diffractive network can execute multiple linear transformations simultaneously or sequentially. This technology allows for the approximation of unique linear transforms with negligible errors, and the spectral multiplexing capability can be increased by increasing the number of diffractive neurons.
ADVANCED PHOTONICS
(2023)
Article
Automation & Control Systems
Md Sadman Sakib Rahman, Aydogan Ozcan
Summary: This study demonstrates for the first time a time-lapse image classification scheme using a diffractive network, improving classification accuracy and generalization performance by leveraging the lateral movements of the input objects and/or the diffractive network. Numerical exploration reveals a blind testing accuracy of 62.03% on the optical classification of objects from the CIFAR-10 dataset using time-lapse diffractive networks, achieving the highest inference accuracy so far. Time-lapse diffractive networks will be widely beneficial for spatiotemporal analysis of input signals using all-optical processors.
ADVANCED INTELLIGENT SYSTEMS
(2023)
Article
Mathematical & Computational Biology
Michael John Fanous, Nir Pillar, Aydogan Ozcan
Summary: Traditional staining methods have drawbacks, while computational virtual staining using deep learning techniques has emerged as a powerful solution. Virtual staining can be combined with neural networks to correct microscopy aberrations and enhance resolution, significantly improving sample preparation and imaging in biomedical microscopy.
FRONTIERS IN BIOINFORMATICS
(2023)
