The editorial introduces the joint theme issue of Advanced Photonics and Advanced Photonics Nexus, “Photonics and AI,” which showcases the latest research at the intersection of these two disciplines.

The 2024 Nobel Prize in Physics recognized John Hopfield and Geoffrey Hinton for their pioneering work on artificial neural networks, which profoundly impacted the physical sciences, particularly optics and photonics. This perspective summarizes the Nobel laureates’ contributions, highlighting the physics-based principles and inspiration behind the development of modern artificial intelligence (AI) and also outlining some of the emerging major advances achieved in optics and photonics enabled by AI.

Demetri Psaltis (École Polytechnique Fédérale de Lausanne) discusses advances in optical computing, in conversation with Guohai Situ (Shanghai Institute of Optics and Fine Mechanics, Chinese Academy of Sciences), for the Advanced Photonics theme issue on AI and Photonics.

The article comments on a recently developed neural network that enables ultrathin flat optics imaging in full color.

The article comments on a novel device recently reported in Nature, which combines a dielectric bowtie nanoantenna with a twisted lattice nanocavity to achieve sub-diffraction-limited mode volumes.

Metal halide perovskite materials have rapidly advanced in the perovskite solar cells and light-emitting diodes due to their superior optoelectronic properties. The structure of perovskite optoelectronic devices includes the perovskite active layer, electron transport layer, and hole transport layer. This indicates that the optimization process unfolds as a complex interplay between intricate chemical crystallization processes and sophisticated physical mechanisms. Traditional research in perovskite optoelectronics has mainly depended on trial-and-error experimentation, a less efficient approach. Recently, the emergence of machine learning (ML) has drastically streamlined the optimization process. Due to its powerful data processing capabilities, ML has significant advantages in uncovering potential patterns and making predictions. More importantly, ML can reveal underlying patterns in data and elucidate complex device mechanisms, playing a pivotal role in enhancing device performance. We present the latest advancements in applying ML to perovskite optoelectronic devices, covering perovskite active layers, transport layers, interface engineering, and mechanisms. In addition, it offers a prospective outlook on future developments. We believe that the deep integration of ML will significantly expedite the comprehensive enhancement of perovskite optoelectronic device performance.

Planar cameras with high performance and wide field of view (FOV) are critical in various fields, requiring highly compact and integrated technology. Existing wide FOV metalenses show great potential for ultrathin optical components, but there is a set of tricky challenges, such as chromatic aberrations correction, central bright speckle removal, and image quality improvement of wide FOV. We design a neural meta-camera by introducing a knowledge-fused data-driven paradigm equipped with transformer-based network. Such a paradigm enables the network to sequentially assimilate the physical prior and experimental data of the metalens, and thus can effectively mitigate the aforementioned challenges. An ultra-wide FOV meta-camera, integrating an off-axis monochromatic aberration-corrected metalens with a neural CMOS image sensor without any relay lenses, is employed to demonstrate the availability. High-quality reconstructed results of color images and real scene images at different distances validate that the proposed meta-camera can achieve an ultra-wide FOV (>100 deg) and full-color images with the correction of chromatic aberration, distortion, and central bright speckle, and the contrast increase up to 13.5 times. Notably, coupled with its compact size (< 0.13 cm3), portability, and full-color imaging capacity, the neural meta-camera emerges as a compelling alternative for applications, such as micro-navigation, micro-endoscopes, and various on-chip devices.

The global chip industry is grappling with dual challenges: a profound shortage of new chips and a surge of counterfeit chips valued at $75 billion, introducing substantial risks of malfunction and unwanted surveillance. To counteract this, we propose an optical anti-counterfeiting detection method for semiconductor devices that is robust under adversarial tampering features, such as malicious package abrasions, compromised thermal treatment, and adversarial tearing. Our new deep-learning approach uses a RAPTOR (residual, attention-based processing of tampered optical response) discriminator, showing the capability of identifying adversarial tampering to an optical, physical unclonable function based on randomly patterned arrays of gold nanoparticles. Using semantic segmentation and labeled clustering, we efficiently extract the positions and radii of the gold nanoparticles in the random patterns from 1000 dark-field images in just 27 ms and verify the authenticity of each pattern using RAPTOR in 80 ms with 97.6% accuracy under difficult adversarial tampering conditions. We demonstrate that RAPTOR outperforms the state-of-the-art Hausdorff, Procrustes, and average Hausdorff distance metrics, achieving a 40.6%, 37.3%, and 6.4% total accuracy increase, respectively.

Quantitative phase imaging (QPI) is a label-free technique that provides optical path length information for transparent specimens, finding utility in biology, materials science, and engineering. Here, we present QPI of a three-dimensional (3D) stack of phase-only objects using a wavelength-multiplexed diffractive optical processor. Utilizing multiple spatially engineered diffractive layers trained through deep learning, this diffractive processor can transform the phase distributions of multiple two-dimensional objects at various axial positions into intensity patterns, each encoded at a unique wavelength channel. These wavelength-multiplexed patterns are projected onto a single field of view at the output plane of the diffractive processor, enabling the capture of quantitative phase distributions of input objects located at different axial planes using an intensity-only image sensor. Based on numerical simulations, we show that our diffractive processor could simultaneously achieve all-optical QPI across several distinct axial planes at the input by scanning the illumination wavelength. A proof-of-concept experiment with a 3D-fabricated diffractive processor further validates our approach, showcasing successful imaging of two distinct phase objects at different axial positions by scanning the illumination wavelength in the terahertz spectrum. Diffractive network-based multiplane QPI designs can open up new avenues for compact on-chip phase imaging and sensing devices.

Optical superoscillation enables far-field superresolution imaging beyond diffraction limits. However, existing superoscillatory lenses for spatial superresolution imaging systems still confront critical performance limitations due to the lack of advanced design methods and limited design degree of freedom. Here, we propose an optical superoscillatory diffractive neural network (SODNN) that achieves spatial superresolution for imaging beyond the diffraction limit with superior optical performance. SODNN is constructed by utilizing diffractive layers for optical interconnections and imaging samples or biological sensors for nonlinearity. This modulates the incident optical field to create optical superoscillation effects in three-dimensional (3D) space and generate the superresolved focal spots. By optimizing diffractive layers with 3D optical field constraints under an incident wavelength size of λ, we achieved a superoscillatory optical spot and needle with a full width at half-maximum of 0.407λ at the far-field distance over 400λ without sidelobes over the field of view and with a long depth of field over 10λ. Furthermore, the SODNN implements a multiwavelength and multifocus spot array that effectively avoids chromatic aberrations, achieving comprehensive performance improvement that surpasses the trade-off among performance indicators of conventional superoscillatory lens design methods. Our research work will inspire the development of intelligent optical instruments to facilitate the applications of imaging, sensing, perception, etc.

Optical computing is considered a promising solution for the growing demand for parallel computing in various cutting-edge fields that require high integration and high-speed computational capacity. We propose an optical computation architecture called diffraction casting (DC) for flexible and scalable parallel logic operations. In DC, a diffractive neural network is designed for single instruction, multiple data (SIMD) operations. This approach allows for the alteration of logic operations simply by changing the illumination patterns. Furthermore, it eliminates the need for encoding and decoding of the input and output, respectively, by introducing a buffer around the input area, facilitating end-to-end all-optical computing. We numerically demonstrate DC by performing all 16 logic operations on two arbitrary 256-bit parallel binary inputs. Additionally, we showcase several distinctive attributes inherent in DC, such as the benefit of cohesively designing the diffractive elements for SIMD logic operations that assure high scalability and high integration capability. Our study offers a design architecture for optical computers and paves the way for a next-generation optical computing paradigm.

Machine-learning techniques have gained popularity in nanophotonics research, being applied to predict optical properties, and inversely design structures. However, one limitation is the cost of acquiring training data, as complex structures require time-consuming simulations. To address this, researchers have explored using transfer learning, where pretrained networks can facilitate convergence with fewer data for related tasks, but application to more difficult tasks is still limited. In this work, a nested transfer learning approach is proposed, training models to predict structures of increasing complexity, with transfer between each model and few data used at each step. This allows modeling thin film stacks with higher optical complexity than previously reported. For the forward model, a bidirectional recurrent neural network is utilized, which excels in modeling sequential inputs. For the inverse model, a convolutional mixture density network is employed. In both cases, a relaxed choice of materials at each layer is introduced, making the approach more versatile. The final nested transfer models display high accuracy in retrieving complex arbitrary spectra and matching idealized spectra for specific application-focused cases, such as selective thermal emitters, while keeping data requirements modest. Our nested transfer learning approach represents a promising avenue for addressing data acquisition challenges.

Despite more than 40 years of development, it remains difficult for optical logic computing to support more than four operands because the high parallelism of light has not been fully exploited in current methods that are restrained by inefficient optical nonlinearity and redundant input modulation. In this paper, we propose a large-scale optical programmable logic array (PLA) based on parallel spectrum modulation. By fully exploiting the wavelength resource, an eight-input PLA is experimentally demonstrated with 256 wavelength channels. And it is extended to nine-input PLA through the combination of wavelength’s and spatial dimensions. Based on PLA, many advanced logic functions like 8-256 decoder, 4-bit comparator, adder and multiplier, and state machines are first realized in optics. We implement the two-dimensional optical cellular automaton (CA) for what we believe is the first time and run Conway’s Game of Life to simulate the complex evolutionary processes (pulsar explosion, glider gun, and breeder). Other CA models, such as the replicator-like evolution and the nonisotropic evolution to generate the Sierpinski triangle are also demonstrated. Our work significantly alleviates the challenge of scalability in optical logic devices and provides a universal optical computing platform for two-dimensional CA.

Photon avalanche occurring in lanthanide-doped materials exhibits a giant optical nonlinear response of the emission intensity to the excitation intensity, which holds great potential in the applications of optical sensing, super-resolution imaging, quantum detection, and other techniques. However, strategies for developing photon avalanches in nanoparticles are limited, and many widely used lanthanide ions have not yet been able to generate high-efficiency avalanching emissions. A general strategy named cascade migrating photon avalanche was proposed to achieve efficient avalanching emissions with huge optical nonlinearities from a large number of emitters at the nanoscale and at room temperature. Specifically, the optical nonlinearity order of bright avalanched Tm3 + -emission was achieved at 63rd order by utilizing the Yb3 + / Pr3 + -codoped nano-engine. By further incorporating a Gd3 + sublattice migrating network, its avalanching energy can propagate over a long distance to arouse avalanching emission with extreme optical nonlinearities up to 45th order among various emitters (Tb3 + , Eu3 + , Dy3 + , Sm3 + ) in multilayered nanostructures. By achieving abundant avalanching full-spectrum emissions, it would be highly conducive to applications in various fields. For instance, our strategy demonstrated its applicability in multi-color super-resolution microscopic imaging with single-nanoparticle sensitivity and resolution up to 48 nm, utilizing a single low-power 852 nm excitation beam.

Nondeterministic-polynomial-time (NP)-complete problems are widely involved in various real-life scenarios but are still intractable in being solved efficiently on conventional computers. It is of great practical significance to construct versatile computing architectures that solve NP-complete problems with computational advantage. Here, we present a reconfigurable integrated photonic processor to efficiently solve a benchmark NP-complete problem, the subset sum problem. We show that in the case of successive primes, the photonic processor has genuinely surpassed electronic processors launched recently by taking advantage of the high propagation speed and vast parallelism of photons and state-of-the-art integrated photonic technology. Moreover, we are able to program the photonic processor to tackle different problem instances, relying on the tunable integrated modules, variable split junctions, which can be used to build a fully reconfigurable architecture potentially allowing 2N configurations at most. Our experiments confirm the potential of the photonic processor as a versatile and efficient computing platform, suggesting a possible practical route to solving computationally hard problems at a large scale.

On-chip quantum information network requires qubit transfer between different wavelengths while preserving quantum coherence and entanglement, which requires the availability of broadband upconversion. Herein, we demonstrate a mode-hybridization-based broadband nonlinear frequency conversion on X-cut thin film lithium niobate. With the spontaneous quasi-phase matching and quasi-group-velocity matching being simultaneously satisfied, broadband second-harmonic generation with a 3-dB bandwidth up to 13 nm has been achieved in a micro-racetrack resonator. The same mechanism can work on the frequency conversion of the ultrashort pulse in the bent waveguide structure. This work will be beneficial to on-chip tunable frequency conversion and quantum light source generation on integrated photonic platforms and further enable on-chip large-capacity multiplexing, multichannel optical information processing, and large quantum information networks.