High-throughput terahertz imaging: progress and challenges

Light Science & Applications, Год журнала: 2023, Номер 12(1)

Опубликована: Сен. 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.

Язык: Английский

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Diffractive optical computing in free space

Nature Communications, Год журнала: 2024, Номер 15(1)

Опубликована: Фев. 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.

Язык: Английский

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All-optical image denoising using a diffractive visual processor

Light Science & Applications, Год журнала: 2024, Номер 13(1)

Опубликована: Фев. 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.

Язык: Английский

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All-optical complex field imaging using diffractive processors

Light Science & Applications, Год журнала: 2024, Номер 13(1)

Опубликована: Май 28, 2024

Язык: Английский

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Universal linear intensity transformations using spatially incoherent diffractive processors

Light Science & Applications, Год журнала: 2023, Номер 12(1)

Опубликована: Авг. 15, 2023

Abstract Under spatially coherent light, a diffractive optical network composed of structured surfaces can be designed to perform any arbitrary complex-valued linear transformation between its input and output fields-of-view (FOVs) if the total number ( N ) optimizable phase-only features is ≥~2 i o , where refer useful pixels at FOVs, respectively. Here we report design incoherent processor that approximate in time-averaged intensity FOVs. monochromatic varying point spread function H network, corresponding given, arbitrarily-selected transformation, written as m n ; ′, ′) = | h ′)| 2 same define coordinates Using numerical simulations deep learning, supervised through examples input-output profiles, demonstrate trained all-optically ≥ ~2 . We also networks for processing information multiple illumination wavelengths, operating simultaneously. Finally, numerically performs all-optical classification handwritten digits under illumination, achieving test accuracy >95%. Spatially will broadly designing visual processors work natural light.

Язык: Английский

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Nonlinear encoding in diffractive information processing using linear optical materials

Light Science & Applications, Год журнала: 2024, Номер 13(1)

Опубликована: Июль 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

Язык: Английский

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Multispectral Quantitative Phase Imaging Using a Diffractive Optical Network

Advanced Intelligent Systems, Год журнала: 2023, Номер 5(11)

Опубликована: Авг. 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.

Язык: Английский

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Multilevel design and construction in nanomembrane rolling for three-dimensional angle-sensitive photodetection

Nature Communications, Год журнала: 2024, Номер 15(1)

Опубликована: Апрель 9, 2024

Releasing pre-strained two-dimensional nanomembranes to assemble on-chip three-dimensional devices is crucial for upcoming advanced electronic and optoelectronic applications. However, the release process affected by many unclear factors, hindering transition from laboratory industrial Here, we propose a quasistatic multilevel finite element modeling structures offer verification results various bilayer nanomembranes. Take Si/Cr nanomembrane as an example, confirm that structural formation governed both minimum energy state geometric constraints imposed edges of sacrificial layer. Large-scale, high-yield fabrication achieved, two distinct are assembled same precursor. Six types photodetectors then prepared resolve incident angle light with deep neural network model, opening up possibilities design manufacturing methods More-than-Moore-era devices.

Язык: Английский

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Information-hiding cameras: Optical concealment of object information into ordinary images

Science Advances, Год журнала: 2024, Номер 10(24)

Опубликована: Июнь 12, 2024

We introduce an information-hiding camera integrated with electronic decoder that is jointly optimized through deep learning. This system uses a diffractive optical processor, which transforms and hides input images into ordinary-looking patterns deceive/mislead observers. transformation valid for infinitely many combinations of secret messages, transformed output passive light-matter interactions within the processor. By processing these patterns, network accurately reconstructs original information hidden deceptive output. demonstrated our approach by designing cameras operating under various lighting conditions noise levels, showing their robustness. further extended this framework to multispectral operation, allowing concealment decoding multiple at different wavelengths, performed simultaneously. The feasibility was also validated experimentally using terahertz radiation. encoder–electronic decoder-based codesign provides high speed energy efficient camera, offering powerful solution visual security.

Язык: Английский

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Pyramid diffractive optical networks for unidirectional image magnification and demagnification

Light Science & Applications, Год журнала: 2024, Номер 13(1)

Опубликована: Июль 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.

Язык: Английский

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