Cited by All-Optical Phase Conjugation Using Diffractive Wavefront Processing

High-throughput terahertz imaging: progress and challenges DOI

Xurong Li, Jingxi Li, Yuhang Li

et al.

Light Science & Applications, Journal Year: 2023, Volume and Issue: 12(1)

Published: Sept. 15, 2023

Abstract Many exciting terahertz imaging applications, such as non-destructive evaluation, biomedical diagnosis, and security screening, have been historically limited in practical usage due to the raster-scanning requirement of systems, which impose very low speeds. However, recent advancements systems greatly increased throughput brought promising potential radiation from research laboratories closer real-world applications. Here, we review development technologies both hardware computational perspectives. We introduce compare different types enabling frequency-domain time-domain using various thermal, photon, field image sensor arrays. discuss how algorithms provide opportunities for capturing time-of-flight, spectroscopic, phase, intensity data at high throughputs. Furthermore, new prospects challenges future high-throughput are briefly introduced.

Language: Английский

Citations

Matrix Diffractive Deep Neural Networks Merging Polarization into Meta‐Devices DOI

Yuzhong Wang,

Axiang Yu,

Yayun Cheng

et al.

Laser & Photonics Review, Journal Year: 2023, Volume and Issue: 18(2)

Published: Oct. 25, 2023

Abstract The all‐optical diffractive deep neural networks (D 2 NNs) framework as a hardware platform is demonstrated to implement various advanced functional meta‐devices with high parallelism and processing speed. However, the design methodology merging trainable polarization modulation neurons into D NNs, which potentially possess higher integration more task‐loading capacity, not yet fully explored. Here, matrix (M‐D are proposed deploy polarization‐sensitive Jones metasurfaces multiplexing perform sophisticated inference tasks well inverse designs for meta‐devices. Three functionalities implemented by M‐D that is, task‐capacity classification, non‐interleaved high‐efficiency eight‐channel regulation, custom‐polarization information cryptographic multiplexing. NNs provide new strategy merge electromagnetic optical field modulators metasurfaces, may drive evolution of toward multi‐task devices.

Language: Английский

Citations

Diffractive optical computing in free space DOI

Jingtian Hu, Deniz Mengü, Dimitrios C. Tzarouchis

et al.

Nature Communications, Journal Year: 2024, Volume and Issue: 15(1)

Published: Feb. 20, 2024

Abstract Structured optical materials create new computing paradigms using photons, with transformative impact on various fields, including machine learning, computer vision, imaging, telecommunications, and sensing. This Perspective sheds light the potential of free-space systems based engineered surfaces for advancing computing. Manipulating in unprecedented ways, emerging structured enable all-optical implementation mathematical functions learning tasks. Diffractive networks, particular, bring deep-learning principles into design operation to functionalities. Metasurfaces consisting deeply subwavelength units are achieving exotic responses that provide independent control over different properties can major advances computational throughput data-transfer bandwidth processors. Unlike integrated photonics-based optoelectronic demand preprocessed inputs, processors have direct access all degrees freedom carry information about an input scene/object without needing digital recovery or preprocessing information. To realize full architectures, diffractive metasurfaces need advance symbiotically co-evolve their designs, 3D fabrication/integration, cascadability, accuracy serve needs next-generation computing, telecommunication technologies.

Language: Английский

Citations

All-optical image denoising using a diffractive visual processor DOI

Çağatay Işıl, Tianyi Gan,

F. Onuralp Ardic

et al.

Light Science & Applications, Journal Year: 2024, Volume and Issue: 13(1)

Published: Feb. 4, 2024

Abstract 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 several iterations implemented in, e.g., graphics processing units (GPUs). While deep learning-enabled methods can operate non-iteratively, they also introduce and impose a significant computational burden, leading increased power consumption. Here, we an analog diffractive denoiser all-optically non-iteratively clean various forms noise artifacts images – at speed light propagation within thin visual processor that axially spans <250 × λ, where λ is wavelength light. This all-optical comprises passive transmissive layers optimized using learning physically scatter optical modes represent features, causing them miss output Field-of-View (FoV) while retaining object features interest. Our results show these denoisers efficiently salt pepper rendering-related spatial phase or intensity achieving efficiency ~30–40%. We experimentally demonstrated effectiveness this architecture 3D-printed operating terahertz spectrum. Owing their speed, power-efficiency, minimal overhead, be transformative for display projection systems, including, holographic displays.

Language: Английский

Citations

All-optical complex field imaging using diffractive processors DOI

Jingxi Li, Yuhang Li, Tianyi Gan

et al.

Light Science & Applications, Journal Year: 2024, Volume and Issue: 13(1)

Published: May 28, 2024

Language: Английский

Citations

Data‐Class‐Specific All‐Optical Transformations and Encryption DOI

Bijie Bai, Heming Wei, Xilin Yang

et al.

Advanced Materials, Journal Year: 2023, Volume and Issue: 35(31)

Published: April 26, 2023

Abstract Diffractive optical networks provide rich opportunities for visual computing tasks. Here, data‐class‐specific transformations that are all‐optically performed between the input and output fields‐of‐view (FOVs) of a diffractive network presented. The information objects is encoded into amplitude ( A ), phase P or intensity I ) field at input, which processed by network. At output, an image sensor‐array directly measures transformed patterns, encrypted using transformation matrices preassigned to different data classes, i.e., separate matrix each class. original images can be recovered applying correct decryption key (the inverse transformation) corresponding matching class, while any other will lead loss information. All‐optical class‐specific covering → , various datasets numerically demonstrated. feasibility this framework also experimentally validated fabricating successfully tested parts electromagnetic spectrum, 1550 nm 0.75 mm wavelengths. Data‐class‐specific all‐optical fast energy‐efficient method encryption, enhancing security privacy.

Language: Английский

Citations

Pyramid diffractive optical networks for unidirectional image magnification and demagnification DOI

Bijie Bai, Xilin Yang, Tianyi Gan

et al.

Light Science & Applications, Journal Year: 2024, Volume and Issue: 13(1)

Published: July 31, 2024

Abstract Diffractive deep neural networks (D 2 NNs) are composed of successive transmissive layers optimized using supervised learning to all-optically implement various computational tasks between an input and output field-of-view. Here, we present a pyramid-structured diffractive optical network design (which term P-D NN), specifically for unidirectional image magnification demagnification. In this design, the pyramidally scaled in alignment with direction or This NN creates high-fidelity magnified demagnified images only one direction, while inhibiting formation opposite direction—achieving desired imaging operation much smaller number degrees freedom within processor volume. Furthermore, maintains its magnification/demagnification functionality across large band illumination wavelengths despite being trained single wavelength. We also designed wavelength-multiplexed NN, where magnifier demagnifier operate simultaneously directions, at two distinct wavelengths. demonstrate that by cascading multiple modules, can achieve higher factors. The efficacy architecture was validated experimentally terahertz illumination, successfully matching our numerical simulations. offers physics-inspired strategy designing task-specific visual processors.

Language: Английский

Citations

Multispectral Quantitative Phase Imaging Using a Diffractive Optical Network DOI

Che‐Yung Shen, Jingxi Li, Deniz Mengü

et al.

Advanced Intelligent Systems, Journal Year: 2023, Volume and Issue: 5(11)

Published: Aug. 25, 2023

As a label‐free imaging technique, quantitative phase (QPI) provides optical path length information of transparent specimens for various applications in biology, materials science, and engineering. Multispectral QPI measures across multiple spectral bands, permitting the examination wavelength‐specific dispersion characteristics samples. Herein, design diffractive processor is presented that can all‐optically perform multispectral phase‐only objects within snapshot. The utilizes spatially engineered layers, optimized through deep learning, to encode profile input object at predetermined set wavelengths into spatial intensity variations output plane, allowing using monochrome focal plane array. Through numerical simulations, processors are demonstrated simultaneously 9 16 target bands visible spectrum. generalization these designs validated tests on unseen objects, including thin Pap smear images. Due its all‐optical processing capability passive dielectric materials, this offers compact power‐efficient solution high‐throughput microscopy spectroscopy.

Language: Английский

Citations

Nonlinear encoding in diffractive information processing using linear optical materials DOI

Yuhang Li, Jingxi Li, Aydogan Özcan

et al.

Light Science & Applications, Journal Year: 2024, Volume and Issue: 13(1)

Published: July 23, 2024

Abstract Nonlinear encoding of optical information can be achieved using various forms data representation. Here, we analyze the performances different nonlinear strategies that employed in diffractive processors based on linear materials and shed light their utility performance gaps compared to state-of-the-art digital deep neural networks. For a comprehensive evaluation, used datasets compare statistical inference simpler-to-implement involve, e.g., phase encoding, against repetition-based strategies. We show repetition within volume (e.g., through an cavity or cascaded introduction input data) causes loss universal transformation capability processor. Therefore, blocks cannot provide analogs fully connected convolutional layers commonly However, they still effectively trained for specific tasks achieve enhanced accuracy, benefiting from information. Our results also reveal without provides simpler strategy with comparable accuracy processors. analyses conclusions would broad interest explore push-pull relationship between material-based systems visual

Language: Английский

Citations

All-optical phase conjugation using diffractive wavefront processing DOI

Che‐Yung Shen, Jingxi Li, Tianyi Gan

et al.

Nature Communications, Journal Year: 2024, Volume and Issue: 15(1)

Published: June 11, 2024

Language: Английский

Citations