Free-form optimization of nanophotonic devices: from classical methods to deep learning

Differentiable wave propagation method for shape optimization of freeform optics beyond the paraxial approximation

Freeform optics provides precise control of light for beam shaping and mode conversion, but designing volumetric micro-optical elements requires efficient, low-runtime simulation methods. The wave propagation method (WPM) offers a fast alternative to full-wave solvers for high-index-contrast and non-paraxial systems but lacks differentiability, preventing its use with gradient descent methods for inverse design. We propose a differentiable formulation of WPM using smoothstep functions to create a soft partition of the spatial domain, allowing for gradient-based optimization. This crucial modification enables a general and computationally efficient framework for solving inverse design problems in micro-optics. We demonstrate its effectiveness through the simulation of a freeform hologram exhibiting high diffraction angles, highlighting the limitations of traditional paraxial methods.

Optical multilayer thin film structure inverse design: From optimization to deep learning

Optical multilayer thin film structures have been widely used in numerous photonic domains and applications. The key component to enable these applications is the inverse design. Different from other photonic structures such as metasurface or waveguide, multilayer thin film is a one-dimensional structure, which deserves its own treatment for the design process. Optimization has always been the standard design algorithm for decades. Recent years have witnessed a rapid development of integrating different deep learning algorithms to tackle the inverse design problems. A natural question to ask is: how do these algorithms differ from each other? Why do we need to develop so many algorithms and what type of challenges do they solve? What is the state-of-the-art algorithm in this domain? Here, we review recent progress and provide a guide tour through this research area, starting from traditional optimization to recent deep learning approaches. Challenges and future perspectives are also discussed.

Nanophotonic device design based on large language models: multilayer and metasurface examples

Large language models (LLMs) have gained significant prominence in language processing, demonstrating remarkable performance across a wide range of tasks. Recently, LLMs have been explored in various scientific fields beyond language-based tasks. However, their application in the design of nanophotonic devices remains less explored. Here, we investigate the capabilities of LLMs to address nanophotonic design problems without requiring domain-specific expertise of the user. Our findings show that an LLM with in-context learning enables nonexpert users to calculate optical responses of multilayer films via numerical simulations. Through conversational interaction and feedback between the LLM and the user, an optimal design of the multilayer films can be also produced for the user-provided target optical properties. Furthermore, we fine-tune the LLM using text-based representations of the structure and properties of optical metasurfaces. We demonstrate that the fine-tuned LLM can generate metasurface designs with target properties by reversing the input and output text. This research highlights the potential of LLMs to expedite the nanophotonic design process and to make it more accessible to a wider audience.

Free-form catenary-inspired meta-couplers for ultra-high or broadband vertical coupling

Metasurface-assisted waveguide couplers, or meta-couplers, innovatively link free-space optics with on-chip devices, offering flexibility for polarization and wavelength (de)multiplexing, mode-selective coupling, and guided mode manipulation. However, conventional meta-couplers still face challenges with low coupling efficiency and narrow bandwidth due to critical near-field coupling caused by waveguide constraints and unit-cell-based design approach, which cannot be accurately addressed using traditional design methods. In this paper, quasi-continuous dielectric catenary arrays are first employed to enhance efficiency and bandwidth by addressing adjacent coupling issues of discrete metasurface. Then, diffraction analysis demonstrates that the performance of forward-designed couplers is hindered by spurious diffraction orders and destructive interference. To further enhance performance, an adjoint-based topology optimization algorithm is utilized to customize electric near-field, which can effectively suppress spurious diffraction orders and destructive near-field interference, achieving ultra-high coupling efficiency of 93 % with 16.7 dB extinction ratios at 1,550 nm. Additionally, a broadband meta-coupler exceeds 350 nm bandwidth with 50 % average coupling efficiency across O- to L-bands using multiobjective optimization. These high-performance devices may render them suitable for applications in optical communications, sensing, and nonlinear optics. Moreover, the inverse design method shows potential for improving the performance of various metasurface-integrated on-chip devices.

Exploring AI in metasurface structures with forward and inverse design

As an artificially manufactured planar device, a metasurface structure can produce unusual electromagnetic responses by harnessing four basic characteristics of the light wave. Traditional design processes rely on numerical algorithms combined with parameter optimization. However, such methods are often time-consuming and struggle to match actual responses. This paper aims to give a unique perspective to classify the artificial intelligence(AI)-enabled design, dividing it into forward and inverse designs according to the mapping relationship between variables and performance. Forward designs are driven by intelligent algorithms; neural networks are one of the principal ways to realize reverse design. This paper reviews recent progress in AI-enabled metasurface design, examining the principles, advantages, and potential applications. A rich content and detailed comparison can help build a holistic understanding of metasurface design. Moreover, the authors believe that this systematic and detailed review will pave the way for future research and the selection of practical applications.

Empowering nanophotonic applications via artificial intelligence: pathways, progress, and prospects

Empowering nanophotonic devices via artificial intelligence (AI) has revolutionized both scientific research methodologies and engineering practices, addressing critical challenges in the design and optimization of complex systems. Traditional methods for developing nanophotonic devices are often constrained by the high dimensionality of design spaces and computational inefficiencies. This review highlights how AI-driven techniques provide transformative solutions by enabling the efficient exploration of vast design spaces, optimizing intricate parameter systems, and predicting the performance of advanced nanophotonic materials and devices with high accuracy. By bridging the gap between computational complexity and practical implementation, AI accelerates the discovery of novel nanophotonic functionalities. Furthermore, we delve into emerging domains, such as diffractive neural networks and quantum machine learning, emphasizing their potential to exploit photonic properties for innovative strategies. The review also examines AI's applications in advanced engineering areas, e.g., optical image recognition, showcasing its role in addressing complex challenges in device integration. By facilitating the development of highly efficient, compact optical devices, these AI-powered methodologies are paving the way for next-generation nanophotonic systems with enhanced functionalities and broader applications.

Recent Advances in Electromagnetic Devices: Design and Optimization

Electromagnetic devices are a continuous driving force in cutting-edge research and technology, finding applications in diverse fields such as optics [...].

Multiwavelength Achromatic Deflector in the Visible Using a Single-Layer Freeform Metasurface

Deflectors are essential for modulating beam direction in optical systems but often face form factor issues or chromatic aberration with conventional optical elements, such as prisms, mirrors, and diffractive/holographic optical elements. Despite recent efforts to address such issues using metasurfaces, their practicality remains limited due to operation wavelengths in the near-infrared or the fabrication difficulties inherent in the multilayer scheme. Here, we propose a novel single-layer metasurface achieving multiwavelength chromatic aberration-free deflection across the visible spectrum by employing the robust freeform design strategy to simplify the fabrication process. By properly selecting diffraction orders for red, green, and blue wavelengths to achieve identical wavelength-diffraction-order products, the metasurface deflects light at a consistent angle of 41.3° with a high efficiency. The coupled Bloch mode analysis explains the physical properties, and experimental fabrication and characterization confirm its effectiveness. This approach holds potential for various applications such as AR/VR, digital cameras, and high-quality optical systems.

Low-loss and compact arbitrary-order silicon mode converter based on hybrid shape optimization

Mode converters (MCs) play an essential role in mode-division multiplexing (MDM) systems. Numerous schemes have been developed on the silicon-on-insulator (SOI) platform, yet most of them focus solely on the conversion of fundamental mode to one or two specific higher-order modes. In this study, we introduce a hybrid shape optimization (HSO) method that combines particle swarm optimization (PSO) with adjoint methods to optimize the shape of the S-bend waveguide, facilitating the design of arbitrary-order MCs featuring compactness and high performance. Our approach was validated by designing a series of 13 μm-long MCs, enabling efficient conversion between various TE modes, ranging from TE0 to TE3. These devices can be fabricated in a single lithography step and exhibit robust fabrication tolerances. Experiment results indicate that these converters achieve low insertion losses under 1 dB and crosstalks below -15 dB across bandwidths of 80 nm (TE0-TE1), 62 nm (TE0-TE2), 70 nm (TE0-TE3), 80 nm (TE1-TE2), 55 nm (TE1-TE3), and 75 nm (TE2-TE3). This advancement paves the way for flexible mode conversion, significantly enhancing the versatility of on-chip MDM technologies.

Large-scale photonic inverse design: computational challenges and breakthroughs

Recent advancements in inverse design approaches, exemplified by their large-scale optimization of all geometrical degrees of freedom, have provided a significant paradigm shift in photonic design. However, these innovative strategies still require full-wave Maxwell solutions to compute the gradients concerning the desired figure of merit, imposing, prohibitive computational demands on conventional computing platforms. This review analyzes the computational challenges associated with the design of large-scale photonic structures. It delves into the adequacy of various electromagnetic solvers for large-scale designs, from conventional to neural network-based solvers, and discusses their suitability and limitations. Furthermore, this review evaluates the research on optimization techniques, analyzes their advantages and disadvantages in large-scale applications, and sheds light on cutting-edge studies that combine neural networks with inverse design for large-scale applications. Through this comprehensive examination, this review aims to provide insights into navigating the landscape of large-scale design and advocate for strategic advancements in optimization methods, solver selection, and the integration of neural networks to overcome computational barriers, thereby guiding future advancements in large-scale photonic design.

Sample-efficient inverse design of freeform nanophotonic devices with physics-informed reinforcement learning

Finding an optimal device structure in the vast combinatorial design space of freeform nanophotonic design has been an enormous challenge. In this study, we propose physics-informed reinforcement learning (PIRL) that combines the adjoint-based method with reinforcement learning to improve the sample efficiency by an order of magnitude compared to conventional reinforcement learning and overcome the issue of local minima. To illustrate these advantages of PIRL over other conventional optimization algorithms, we design a family of one-dimensional metasurface beam deflectors using PIRL, exceeding most reported records. We also explore the transfer learning capability of PIRL that further improves sample efficiency and demonstrate how the minimum feature size of the design can be enforced in PIRL through reward engineering. With its high sample efficiency, robustness, and ability to seamlessly incorporate practical device design constraints, our method offers a promising approach to highly combinatorial freeform device optimization in various physical domains.

Unleashing the potential: AI empowered advanced metasurface research

In recent years, metasurface, as a representative of micro- and nano-optics, have demonstrated a powerful ability to manipulate light, which can modulate a variety of physical parameters, such as wavelength, phase, and amplitude, to achieve various functions and substantially improve the performance of conventional optical components and systems. Artificial Intelligence (AI) is an emerging strong and effective computational tool that has been rapidly integrated into the study of physical sciences over the decades and has played an important role in the study of metasurface. This review starts with a brief introduction to the basics and then describes cases where AI and metasurface research have converged: from AI-assisted design of metasurface elements up to advanced optical systems based on metasurface. We demonstrate the advanced computational power of AI, as well as its ability to extract and analyze a wide range of optical information, and analyze the limitations of the available research resources. Finally conclude by presenting the challenges posed by the convergence of disciplines.

Schrödinger's Red Beyond 65,000 Pixel-Per-Inch by Multipolar Interaction in Freeform Meta-Atom through Efficient Neural Optimizer

Freeform nanostructures have the potential to support complex resonances and their interactions, which are crucial for achieving desired spectral responses. However, the design optimization of such structures is nontrivial and computationally intensive. Furthermore, the current "black box" design approaches for freeform nanostructures often neglect the underlying physics. Here, a hybrid data-efficient neural optimizer for resonant nanostructures by combining a reinforcement learning algorithm and Powell's local optimization technique is presented. As a case study, silicon nanostructures with a highly-saturated red color are designed and experimentally demonstrated. Specifically, color coordinates of (0.677, 0.304) in the International Commission on Illumination (CIE) chromaticity diagram - close to the ideal Schrödinger's red, with polarization independence, high reflectance (>85%), and a large viewing angle (i.e., up to ± 25°) is achieved. The remarkable performance is attributed to underlying generalized multipolar interferences within each nanostructure rather than the collective array effects. Based on that, pixel size down to ≈400 nm, corresponding to a printing resolution of 65000 pixels per inch is demonstrated. Moreover, the proposed design model requires only ≈300 iterations to effectively search a thirteen-dimensional (13D) design space - an order of magnitude more efficient than the previously reported approaches. The work significantly extends the free-form optical design toolbox for high-performance flat-optical components and metadevices.

Stochastic and multi-objective design of photonic devices with machine learning

Compact and highly performing photonic devices are characterized by non-intuitive geometries, a large number of parameters, and multiple figures of merit. Optimization and machine learning techniques have been explored to handle these complex designs, but the existing approaches often overlook stochastic quantities. As an example, random fabrication uncertainties critically determines experimental device performance. Here, we present a novel approach for the stochastic multi-objective design of photonic devices combining unsupervised dimensionality reduction and Gaussian process regression. The proposed approach allows to efficiently identify promising alternative designs and model the statistic of their response. Incorporating both deterministic and stochastic quantities into the design process enables a comprehensive analysis of the device and of the possible trade-offs between different performance metrics. As a proof-of-concept, we investigate surface gratings for fiber coupling in a silicon-on-insulator platform, considering variability in structure sizes, silicon thickness, and multi-step etch alignment. We analyze 86 alternative designs presenting comparable performance when neglecting variability, discovering on the contrary marked differences in yield and worst-case figures for both fiber coupling efficiency and back-reflections. Pareto frontiers demonstrating optimized device robustness are identified as well, offering a powerful tool for the design and optimization of photonic devices with stochastic figures of merit.

More Than 30 000-fold Field Enhancement of Terahertz Nanoresonators Enabled by Rapid Inverse Design

The rapid development of 6G communications using terahertz (THz) electromagnetic waves has created a demand for highly sensitive THz nanoresonators capable of detecting these waves. Among the potential candidates, THz nanogap loop arrays show promising characteristics but require significant computational resources for accurate simulation. This requirement arises because their unit cells are 10 times smaller than millimeter wavelengths, with nanogap regions that are 1 000 000 times smaller. To address this challenge, we propose a rapid inverse design method using physics-informed machine learning, employing double deep Q-learning with an analytical model of the THz nanogap loop array. In ∼39 h on a middle-level personal computer, our approach identifies the optimal structure through 200 000 iterations, achieving an experimental electric field enhancement of 32 000 at 0.2 THz, 300% stronger than prior results. Our analytical model-based approach significantly reduces the amount of computational resources required, offering a practical alternative to numerical simulation-based inverse design for THz nanodevices.

Designing Metasurfaces for Efficient Solar Energy Conversion

Metasurfaces have recently emerged as a promising technological platform, offering unprecedented control over light by structuring materials at the nanoscale using two-dimensional arrays of subwavelength nanoresonators. These metasurfaces possess exceptional optical properties, enabling a wide variety of applications in imaging, sensing, telecommunication, and energy-related fields. One significant advantage of metasurfaces lies in their ability to manipulate the optical spectrum by precisely engineering the geometry and material composition of the nanoresonators' array. Consequently, they hold tremendous potential for efficient solar light harvesting and conversion. In this Review, we delve into the current state-of-the-art in solar energy conversion devices based on metasurfaces. First, we provide an overview of the fundamental processes involved in solar energy conversion, alongside an introduction to the primary classes of metasurfaces, namely, plasmonic and dielectric metasurfaces. Subsequently, we explore the numerical tools used that guide the design of metasurfaces, focusing particularly on inverse design methods that facilitate an optimized optical response. To showcase the practical applications of metasurfaces, we present selected examples across various domains such as photovoltaics, photoelectrochemistry, photocatalysis, solar-thermal and photothermal routes, and radiative cooling. These examples highlight the ways in which metasurfaces can be leveraged to harness solar energy effectively. By tailoring the optical properties of metasurfaces, significant advancements can be expected in solar energy harvesting technologies, offering new practical solutions to support an emerging sustainable society.

A Quick Method for Predicting Reflectance Spectra of Nanophotonic Devices via Artificial Neural Network

Nanophotonics use the interaction between light and subwavelength structures to design nanophotonic devices and to show unique optical, electromagnetic, and acoustic properties that natural materials do not have. However, this usually requires considerable expertise and a lot of time-consuming electromagnetic simulations. With the continuous development of artificial intelligence, people are turning to deep learning for designing nanophotonic devices. Deep learning models can continuously fit the correlation function between the input parameters and output, using models with weights and biases that can obtain results in milliseconds to seconds. In this paper, we use finite-difference time-domain for simulations, and we obtain the reflectance spectra from 2430 different structures. Based on these reflectance spectra data, we use neural networks for training, which can quickly predict unseen structural reflectance spectra. The effectiveness of this method is verified by comparing the predicted results to the simulation results. Almost all results maintain the main trend, the MSE of 94% predictions are below 10-3, all are below 10-2, and the MAE of 97% predictions are below 2 × 10-2. This approach can speed up device design and optimization, and provides reference for scientific researchers.

Tensor completion algorithm-aided structural color design

In recent years, structural color has developed rapidly due to its distinct advantages, such as low loss, high spatial resolution and environmental friendliness. Various inverse design methods have been extensively investigated to efficiently design optical structures. However, the optimization method for the inverse design of structural color remains a formidable challenge. Traditional optimization approaches, such as genetic algorithms require time-consuming repetitions of structural simulations. Deep learning-assisted design necessitates prior simulations and large amounts of data, making it less efficient for systems with a small number of features. This study proposes a tensor completion algorithm capable of swiftly and accurately predicting missing datasets based on partially obtained datasets to assist in structural color design. Transforming the complex physical problem of structural color design into a spatial structure relationship problem linking geometric parameters and spectral data. The method utilizes tensor multilinear data analysis to effectively capture the complex relationships associated with geometric parameters and spectral data in higher-order data. Numerical and experimental results demonstrate that the algorithm exhibits high reliability in terms of speed and accuracy for diverse structures, datasets of varying sizes, and different materials, significantly enhancing design efficiency. The proposed algorithm offers a viable solution for inverse design problems involving complex physical systems, thereby introducing a novel approach to the design of photonic devices. Additionally, numerical experiments illustrate that the structural color of cruciform resonators with diamond can overcome the high loss issues observed in traditional dielectric materials within the blue wavelength region and enhance the corrosion resistance of the structure. We achieve a wide color gamut and a high-narrow reflection spectrum nearing 1 by this structure, and the theoretical analysis further verifies that diamond holds great promise in the realm of optics.

Design parameters of free-form color splitters for subwavelength pixelated image sensors

Metasurface-based color splitters are emerging as next-generation optical components for image sensors, replacing classical color filters and microlens arrays. In this work, we report how the design parameters such as the device dimensions and refractive indices of the dielectrics affect the optical efficiency of the color splitters. Also, we report how the design grid resolution parameters affect the optical efficiency and discover that the fabrication of a color splitter is possible even in legacy fabrication facilities with low structure resolutions.

Large area optimization of meta-lens via data-free machine learning

Sub-wavelength diffractive optics, commonly known as meta-optics, present a complex numerical simulation challenge, due to their multi-scale nature. The behavior of constituent sub-wavelength scatterers, or meta-atoms, needs to be modeled by full-wave electromagnetic simulations, whereas the whole meta-optical system can be modeled using ray/ Fourier optics. Most simulation techniques for large-scale meta-optics rely on the local phase approximation (LPA), where the coupling between dissimilar meta-atoms is neglected. Here we introduce a physics-informed neural network, coupled with the overlapping boundary method, which can efficiently model the meta-optics while still incorporating all of the coupling between meta-atoms. We demonstrate the efficacy of our technique by designing 1mm aperture cylindrical meta-lenses exhibiting higher efficiency than the ones designed under LPA. We experimentally validated the maximum intensity improvement (up to 53%) of the inverse-designed meta-lens. Our reported method can design large aperture ( ~ 104 − 105λ ) meta-optics in a reasonable time (approximately 15 minutes on a graphics processing unit) without relying on the LPA.

Zhelyeznyakov and coworkers present a data-free physics-informed neural network to model and optimize the electromagnetic field distribution of large-scale ( ~ 1 mm in diameter) optical meta-lenses. This simplified method can speed up the design of large aperture meta-optics.

Machine learning to optimize additive manufacturing for visible photonics

Additive manufacturing has become an important tool for fabricating advanced systems and devices for visible nanophotonics. However, the lack of simulation and optimization methods taking into account the essential physics of the optimization process leads to barriers for greater adoption. This issue can often result in sub-optimal optical responses in fabricated devices on both local and global scales. We propose that physics-informed design and optimization methods, and in particular physics-informed machine learning, are particularly well-suited to overcome these challenges by incorporating known physics, constraints, and fabrication knowledge directly into the design framework.

Fast and rigorous optical simulation of periodically corrugated light-emitting diodes based on a diffraction matrix method

Increasing the light extraction efficiency has been widely studied for highly efficient organic light-emitting diodes (OLEDs). Among many light-extraction approaches proposed so far, adding a corrugation layer has been considered a promising solution for its simplicity and high effectiveness. While the working principle of periodically corrugated OLEDs can be qualitatively explained by the diffraction theory, dipolar emission inside the OLED structure makes its quantitative analysis challenging, making one rely on finite-element electromagnetic simulations that could require huge computing resources. Here, we demonstrate a new simulation method, named the diffraction matrix method (DMM), that can accurately predict the optical characteristics of periodically corrugated OLEDs while achieving calculation speed that is a few orders of magnitude faster. Our method decomposes the light emitted by a dipolar emitter into plane waves with different wavevectors and tracks the diffraction behavior of waves using diffraction matrices. Calculated optical parameters show a quantitative agreement with those predicted by finite-difference time-domain (FDTD) method. Furthermore, the developed method possesses a unique advantage over the conventional approaches that it naturally evaluates the wavevector-dependent power dissipation of a dipole and is thus capable of identifying the loss channels inside OLEDs in a quantitative manner.

Inverse design of high-NA metalens for maskless lithography

We demonstrate an axisymmetric inverse-designed metalens to improve the performance of zone-plate-array lithography (ZPAL), one of the maskless lithography approaches, that offer a new paradigm for nanoscale research and industry. First, we derive a computational upper bound for a unit-cell-based axisymmetric metalens. Then, we demonstrate a fabrication-compatible inverse-designed metalens with 85.50% transmission normalized focusing efficiency at 0.6 numerical aperture at 405 nm wavelength; a higher efficiency than a theoretical gradient index lens design (79.98%). We also demonstrate experimental validation for our axisymmetric inverse-designed metalens via electron beam lithography. Metalens-based maskless lithography may open a new way of achieving low-cost, large-area nanofabrication.

Exploiting geometric biases in inverse nano-optical problems using artificial neural networks

Solving the inverse problem is a major challenge in contemporary nano-optics. However, frequently not just a possible solution needs to be found but rather the solution that accommodates constraints imposed by the problem at hand. To select the most plausible solution for a nano-optical inverse problem additional information can be used in general, but how to specifically formulate it frequently remains unclear. Here, while studying the reconstruction of the shape of an object using the electromagnetic field in its proximity, we show how to take advantage of artificial neural networks (ANNs) to produce solutions consistent with prior assumptions concerning the structures. By preparing suitable datasets where the specific shapes of possible scatterers are defined, the ANNs learn the underlying scatterer present in the datasets. This helps to find a plausible solution to the otherwise non-unique inverse problem. We show that topology optimization, in contrast, can fail to recover the scatterer geometry meaningfully but a hybrid approach that is based on both, ANNs and a topology optimization, eventually leads to the most promising performance. Our work has direct implications in fields such as optical metrology.

Experimental demonstration of inverse-designed silicon integrated photonic power splitters

The on-chip optical power splitter is a common and important device in photonic integrated circuits (PICs). To achieve a low insertion loss and high uniformity while splitting the guided light, multi-mode interferometer-based structures utilizing a self-imaging principle are widely used mainly in the form of a 1 × 2 configuration. Recently, an inverse design method for nanophotonic devices has emerged to overcome the limited capability of the conventional design methods and make it possible to explore the vast number of design parameters. Because of the non-intuitive shape of inverse-designed structures, they allow us to discover interesting and complex optical responses which are almost impossible to find with conventional design methods. Here, we report two kinds of inverse-designed 1 × 4 optical power splitters composed of silicon bars of different lengths, which are fabricated with a standard CMOS-compatible process. The particle swarm optimization method was used to minimize the insertion loss and divide the power evenly into each output port with finite-difference time-domain method simulation. The first optical power splitter has a compact size of 8.14 × 12 μm2 and the second optical power splitter has an even more compact size of 6.0 × 7.2 μm2. With the inverse designed structures, we fabricated the chip with a CMOS-compatible fabrication process. Experimental verification of the structures is provided and good agreement with the numerical results is obtained. The first 1 × 4 optical power splitter has a low insertion loss of less than 0.76 dB and uniformity of less than 0.84 dB, and the second more compact optical power splitter has a low insertion loss of less than 1.08 dB and uniformity of less than 0.81 dB. As the complexity of on-chip photonic systems has steadily increased, the inverse design of photonic structures holds great potential to be an essential part of advanced design tools.

Dielectric metalens for miniaturized imaging systems: progress and challenges

Lightweight, miniaturized optical imaging systems are vastly anticipated in these fields of aerospace exploration, industrial vision, consumer electronics, and medical imaging. However, conventional optical techniques are intricate to downscale as refractive lenses mostly rely on phase accumulation. Metalens, composed of subwavelength nanostructures that locally control light waves, offers a disruptive path for small-scale imaging systems. Recent advances in the design and nanofabrication of dielectric metalenses have led to some high-performance practical optical systems. This review outlines the exciting developments in the aforementioned area whilst highlighting the challenges of using dielectric metalenses to replace conventional optics in miniature optical systems. After a brief introduction to the fundamental physics of dielectric metalenses, the progress and challenges in terms of the typical performances are introduced. The supplementary discussion on the common challenges hindering further development is also presented, including the limitations of the conventional design methods, difficulties in scaling up, and device integration. Furthermore, the potential approaches to address the existing challenges are also deliberated.

Deep learning approach for inverse design of metasurfaces with a wider shape gamut

While the large design degrees of freedom (DOFs) give metasurfaces a tremendous versatility, they make the inverse design challenging. Metasurface designers mostly rely on simple shapes and ordered placements, which restricts the achievable performance. We report a deep learning based inverse design flow that enables a fuller exploitation of the meta-atom shape. Using a polygonal shape encoding that covers a broad gamut of lithographically realizable resonators, we demonstrate the inverse design of color filters in an amorphous silicon material platform. The inverse-designed transmission-mode color filter metasurfaces are experimentally realized and exhibit enhancement in the color gamut.

Inverse-designed waveguide-based biosensor for high-sensitivity, single-frequency detection of biomolecules

Integrated silicon photonic waveguide biosensors have shown great potential for detecting bio-molecules because they enable efficient device functionalization via a well-developed surface chemistry, as well as simple scalable manufacturing, which makes them particularly suitable for low-cost point-of-care diagnostic. The on-chip integrated biosensors can be broadly classified into two types: (i) high-quality factor resonator sensors and (ii) interferometric sensors relying on non-resonant optical elements such as e.g. integrated waveguides. The former type usually requires a broadband or a tunable light source as well as complicated signal post-processing to measure a shift of the resonance frequency, while the latter exhibits a relatively low sensitivity due to the lack of efficient light recycling and phase accumulation mechanism in low quality factor elements. Additionally, high quality factor resonant photonic structures can be very sensitive to the presence of other non-target molecules in the water solution, causing sensor vulnerability to any noise. In this work, we combine a computational "inverse design" technique and a recently introduced high-contrast probe cleavage detection (HCCD) technique to design and optimize waveguide-based biosensors that demonstrate high sensitivity to the target molecule while being less sensitive to noise. The proposed biosensors only require a single frequency (or narrow-band) source and an intensity detector, which greatly simplifies the detection system, making it suitable for point-of-care applications. The optimal integrated sensor design that we demonstrate shows 98.3% transmission for the positive (target detected, probes cleaved) state and 4.9% transmission for the negative (probes are still attached) state at 1550 nm wavelength. The signal intensity contrast (20.06-fold transmission increase) shown in this work is much greater than the shift of the resonance frequency (less than 1% wavelength shift) observed in conventional ring-resonator-based biosensors. The new design may pave the way for realizing a single-frequency highly sensitive and selective optical biosensor system with a small physical footprint and a simple optical readout on a silicon chip.