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

Terahertz waves offer advantages for nondestructive detection of hidden objects/defects in materials, as they can penetrate most optically-opaque materials. However, existing terahertz inspection systems face throughput and accuracy restrictions due to their limited imaging speed and resolution. Furthermore, machine-vision-based systems using large-pixel-count imaging encounter bottlenecks due to their data storage, transmission and processing requirements. Here, we report a diffractive sensor that rapidly detects hidden defects/objects within a 3D sample using a single-pixel terahertz detector, eliminating sample scanning or image formation/processing. Leveraging deep-learning-optimized diffractive layers, this diffractive sensor can all-optically probe the 3D structural information of samples by outputting a spectrum, directly indicating the presence/absence of hidden structures or defects. We experimentally validated this framework using a single-pixel terahertz time-domain spectroscopy set-up and 3D-printed diffractive layers, successfully detecting unknown hidden defects inside silicon samples. This technique is valuable for applications including security screening, biomedical sensing and industrial quality control. Researchers showcase a deep learning-designed diffractive terahertz sensor that rapidly detects hidden defects within 3D samples based on the output spectrum measured by a single-pixel detector, eliminating sample scanning or image formation/processing.

This paper reports how the spectral linewidths of plasmon resonances can be narrowed down to a few nanometers by optimizing the morphology, surface roughness, and crystallinity of metal nanoparticles (NPs) in two-dimensional (2D) lattices. We developed thermal annealing procedures to achieve ultranarrow surface lattice resonances (SLRs) with full-width at half-maxima linewidths as narrow as 4 nm from arrays of Au, Ag, Al, and Cu NPs. Besides annealing, we developed a chemical vapor deposition process to use Cu NPs as catalytic substrates for graphene growth. Graphene-encapsulated Cu NPs showed the narrowest SLR linewidths (2 nm) and were stable for months. These ultranarrow SLR nanocavity modes supported even narrower lasing emission spectra and high nonlinearity in the input–output light–light curves.

Superposing two or more periodic structures to form moiré patterns is emerging as a promising platform to confine and manipulate light. However, moiré-facilitated interactions and phenomena have been constrained to the vicinity of moiré lattices. Here we report on the observation of ultralong-range coupling between photonic lattices in bilayer and multilayer moiré architectures mediated by dark surface lattice resonances in the vertical direction. We show that two-dimensional plasmonic nanoparticle lattices enable twist-angle-controlled directional lasing emission, even when the lattices are spatially separated by distances exceeding three orders of magnitude of lattice periodicity. Our discovery of far-field interlattice coupling opens the possibility of using the out-of-plane dimension for optical manipulation on the nanoscale and microscale. An ultralong-range coupling was demonstrated between photonic lattices in bilayer and multilayer moiré architectures, which is mediated by dark surface lattice resonances in the vertical direction.

Plasmonic nanostructures with enhanced localized optical fields as well as narrow linewidths have driven advances in numerous applications. However, the active engineering of ultranarrow resonances across the visible regime—and within a single system—has not yet been demonstrated. This paper describes how aluminum nanoparticle arrays embedded in an elastomeric slab may exhibit high-quality resonances with linewidths as narrow as 3 nm at wavelengths not accessible by conventional plasmonic materials. We exploited stretching to improve and tune simultaneously the optical response of as-fabricated nanoparticle arrays by shifting the diffraction mode relative to single-particle dipolar or quadrupolar resonances. This dynamic modulation of particle–particle spacing enabled either dipolar or quadrupolar lattice modes to be selectively accessed and individually optimized. Programmable plasmon modes offer a robust way to achieve real-time tunable materials for plasmon-enhanced molecular sensing and plasmonic nanolasers and opens new possibilities for integrating with flexible electronics.

Nonlinear optical processing of ambient natural light is highly desired for computational imaging and sensing. Strong optical nonlinear response under weak broadband incoherent light is essential for this purpose. By merging 2D transparent phototransistors (TPTs) with liquid crystal (LC) modulators, we create an optoelectronic neuron array that allows self-amplitude modulation of spatially incoherent light, achieving a large nonlinear contrast over a broad spectrum at orders-of-magnitude lower intensity than achievable in most optical nonlinear materials. We fabricated a 10,000-pixel array of optoelectronic neurons, and experimentally demonstrated an intelligent imaging system that instantly attenuates intense glares while retaining the weaker-intensity objects captured by a cellphone camera. This intelligent glare-reduction is important for various imaging applications, including autonomous driving, machine vision, and security cameras. The rapid nonlinear processing of incoherent broadband light might also find applications in optical computing, where nonlinear activation functions for ambient light conditions are highly sought. Merging 2D transparent phototransistors and liquid crystal modulators, authors report an optoelectronic neuron array for self-amplitude modulation of spatially incoherent light, achieving a large nonlinear contrast over a broad spectrum at orders-of-magnitude lower intensity.

The synergistic development of nanophotonics and machine learning has inspired tremendous innovations in both fields in the past decade. In diverse photonics research, deep-learning methods using artificial neural networks become the key game changer that greatly facilitates rapid nanophotonics design and the versatile processing of optical information. Moreover, optical computing platforms that perform calculations through light propagation are receiving tremendous interest as next-generation machine-learning hardware with advantages in computing speed, energy efficiency, and parallelism. This review summarizes the current state-of-the-art nanophotonic devices enabled by machine learning and analyzes the longstanding challenges that must be overcome to make an impact on technology. We also discuss the opportunities of intelligent photonics in applications such as computational imaging/sensing and machine vision. The intersection of nanophotonics with deep learning holds tremendous implications for transformative technologies ranging from internet of things to smart health. Lastly, we provide our perspective on the pressing challenges in intelligent photonics that must be tackled to advance this field to the next level and the vast opportunities for multidisciplinary collaboration.

Traditional data-driven approaches to lithium battery state of health (SOH) estimation face the challenges of difficult feature extraction, insufficient prediction accuracy and weak generalization. To address these issues, this study proposes a novel prediction framework with transfer learning-based linear regression (LR) and a convolutional neural network (CNN) under limited data. In this framework, first, variable inertia weight-based improved particle swarm optimization for variational mode decomposition (VIW-PSO-VMD) is proposed to mitigate the volatility of the “capacity resurgence point” and extract its time-series features. Then, the T-Pearson correlation analysis is introduced to comprehensively analyze the correlations between multivariate features and lithium battery SOH data and accurately extract strongly correlated features to learn the common features of lithium batteries. On this basis, a combination model is proposed, applying LR to extract the trend features and combining them with the multivariate strongly correlated features via a CNN. Transfer learning based on temporal feature analysis is used to improve the cross-domain learning capabilities of the model. We conduct case studies on a NASA dataset and the University of Maryland dataset. The results show that the proposed method is effective in improving the lithium battery SOH estimation accuracy under limited data.

This paper describes how metal–organic frameworks (MOFs) conformally coated on plasmonic nanoparticle arrays can support exciton–plasmon modes with features resembling strong coupling but that are better understood by a weak coupling model. Thin films of Zn-porphyrin MOFs were assembled by dip coating on arrays of silver nanoparticles (NP@MOF) that sustain surface lattice resonances (SLRs). Coupling of excitons with these lattice plasmons led to an SLR-like mixed mode in both transmission and transient absorption spectra. The spectral position of the mixed mode could be tailored by detuning the SLR in different refractive index environments and by changing the periodicity of the nanoparticle array. Photoluminescence showed mode splitting that can be interpreted as modulation of the exciton line shape by the Fano profile of the surface lattice mode, without requiring Rabi splitting. Compared with pristine Zn-porphyrin, hybrid NP@MOF structures achieved a 16-fold enhancement in emission intensity. Our results establish MOFs as a crystalline molecular emitter material that can couple with plasmonic structures for energy exchange and transfer.

To address the critical limitations of conventional band-switching technologies – such as their slow speed, high energy consumption, and mechanical instability – this research introduces a novel deep-learning-driven framework for the intelligent inverse design of polarization-multiplexed metasurfaces. This approach represents a paradigm shift from traditional methods by enabling single-step, computational discovery of metasurface designs that directly encode two distinct optical functions within a single flat device. At the heart of our framework is a custom-designed deep neural network that seamlessly integrates parallel convolutional layers for robust feature extraction with cascaded regression modules for high-precision prediction. This hybrid architecture allows us to engineer sub-wavelength meta-atoms to achieve desired optical responses rigorously. As a groundbreaking demonstration, we designed and optimized a metasurface that achieves dynamic band switching solely through polarization modulation: it generates a targeted transmission peak in the O-band (1,260–1,360 nm) under -polarization and an independent peak in the C-band (1,530–1,565 nm) under -polarization. This mechanism eliminates the need for moving parts. The resulting device exhibits a switching efficiency orders of magnitude greater than its mechanical counterparts, while simultaneously offering enhanced stability, lower power consumption, and inherent adaptability for reconfigurable optical networks. Our work not only validates a specific device but also establishes a robust and generalizable design paradigm, underscoring the transformative potential of uniting deep learning with metasurfaces to achieve ultra-fast, intelligent, and efficient photonic systems for next-generation optical communications.