Metamaterials and Metasurfaces Applications

Derniers papiers

• AbsorbEvo: An Agentic Framework for Autonomous Inverse Design of Microwave Absorbers

  Zhicheng Feng, Yubo Zhao, Xuefeng Yao · 2 octobre 2026

  Designing high-performance microwave absorbers requires specialized expertise in electromagnetic theory, materials science and simulation programming, and entails time-consuming optimization. Here, we present AbsorbEvo, an agentic framework for autonomous inverse design that translates natural-langu…

• Collaborative On-Sensor Array Cameras

  Jipeng Sun, Kaixuan Wei, Thomas Eboli, Congli Wang, Cheng Zheng, Zhihao Zhou, Arka Majumdar, Wolfgang Heidrich, Felix Heide · 7 septembre 2026

  Modern nanofabrication techniques have enabled us to manipulate the wavefront of light with sub-wavelength-scale structures, offering the potential to replace bulky refractive surfaces in conventional optics with ultrathin metasurfaces. In theory, arrays of nanoposts provide unprecedented control ov…

• Direct Optimization of a 3D Finite-Source Reflector via Neural-Network Parameterization

  Roel Hacking, Lisa Kusch, Martijn Anthonissen, Wilbert IJzerman · 2 septembre 2026

  We present a direct optimization method for three-dimensional freeform reflectors that transform the light of a finite-\'etendue source into a prescribed far-field angular intensity distribution. The reflector profile is represented by a small neural network (a multilayer perceptron), which is train…

• Machine Learning Assisted Inverse Design of Pixelated mmWave Patch Antennas

  Nadeem Rather, Holger Claussen, Lester Ho · 25 août 2026

  A machine learning-assisted framework for the inverse design of pixelated millimetre-wave patch antennas targeting the 22--30 GHz band is presented. The antenna surface is represented as a 19x23 binary pixel grid on a Rogers RT/duroid 5880 substrate, where each pixel is either metal or empty, with a…

• A Comprehensive Review of Large Language Models for Nanophotonics: From Surrogate Modeling to Autonomous Design

  Huanshu Zhang, Kegeng Tang, Lei Kang, Sawyer D. Campbell, Zihao Wang, Douglas H. Werner · 20 août 2026

  Metasurfaces have revolutionized the development of photonic devices by enabling unprecedented precision in light manipulation. However, their design processes are often constrained by computationally expensive simulations and complex high-dimensional design spaces. Although deep learning has accele…

• Asymptotics-guided learning and symbolic regression for dispersive resonances

  Konstantinos Alexopoulos, Josselin Garnier · 18 août 2026

  We study resonance prediction in dispersive media, formulated as nonlinear spectral problems for volume integral operators. The main idea is to use asymptotic analysis not only as a baseline approximation, but also as a guide for constructing predictive correction models. We learn the residual betwe…

• Two-Stage Deformable-Convolutional Inverse Design of Nanophotonic Absorbers from Optical Spectra

  Waleed Waseer, Muhammad Shahid Jabbar, Muhammad Sohail Ibrahim, Shujaat Khan · 13 août 2026

  Data-driven inverse design enables efficient generation of nanophotonic structures with prescribed optical responses, but spectrum-to-geometry mapping remains challenging due to non-uniqueness and fine geometric features. This work presents a two-stage deformable-convolutional framework for reconstr…

• DDSNet: Dual-domain Symmetry-aware Network for PCSEL Property Prediction

  Cen Chen, Haitao Huang, Jiazhi Mao, Feifan Xu, Zhe Zhuang, Yuxiang Ren · 29 juillet 2026

  Efficient exploration of the photonic crystal (PhC) lattice design space is essential for developing photonic crystal surface-emitting lasers. While coupled-wave theory (CWT) provides an effective physical framework, its computational cost remains prohibitive for large-scale exploration, driving the…

• A Self-Evolving Agentic Framework for Metasurface Inverse Design

  Yi Huang, Bowen Zheng, Yunxi Dong, Hong Tang, Huan Zhao, S. M. Rakibul Hasan Shawon, Hualiang Zhang · 13 juillet 2026

  Metasurface inverse design can realize complex optical functionality, but turning a target optical response into executable optimization code still requires substantial expertise in computational electromagnetics and solver-specific software engineering. We present a self-evolving agentic framework …

• End-to-End Optimization of Incoherent Imaging for Classification Under Detector-Limited Readout

  Archer Wang, Joshua Chen, Sachin Vaidya, Marin Soljačić · 9 juin 2026

  End-to-end co-optimization of optical front-ends (e.g. metasurfaces) and neural network back-ends has been widely applied to imaging tasks, yet a formalism characterizing when and why such systems outperform conventional lens-based imaging is largely lacking. This paper focuses on object classificat…

• Inverse Design of Realizable Metasurface based Absorbers using Improved Conditioning and Diversity Enhanced Progressively Growing GANs

  Vineetha Joy, Mohammad Abdullah, Pramit Pal, Anshuman Kumar, Amit Sethi, Hema Singh · 5 juin 2026

  Metasurfaces enable precise manipulation of electromagnetic waves for applications such as beam steering, sensing, and stealth technology. However, inverse design of metasurfaces with targeted EM responses remains challenging due to the computational expense of iterative full wave simulation driven …

• Physics Guided Conditional Diffusion Framework for Generative Inverse Design of Manufacturable Metasurface based Absorbers

  Vineetha Joy, Jamshed Palai, Satwik Sahu, Anshuman Kumar, Amit Sethi, Hema Singh · 20 mai 2026

  Inverse design of metasurfaces under continuous electromagnetic constraints requires generation of geometries that simultaneously satisfy stringent spectral specifications and remain manufacturable. Conventional approaches based on iterative full wave simulations are computationally prohibitive for …

• 3D aperture-engineered diffractive neural networks for super-resolution electromagnetic wave computing

  Sheng Gao, Songtao Yang, Haiou Zhang, Yuan Shen, Xing Lin · 20 mai 2026

  The rapid progress in 6G communication and high-bandwidth radar has driven an unprecedented surge in the spatial density of signal sources, resulting in an increasingly congested electromagnetic (EM) environment. When resolving closely spaced signals and interference, existing architectures are stri…

• Neural Adjoint Method for Meta-optics: Accelerating Volumetric Inverse Design via Fourier Neural Operators

  Chanik Kang, Hyewon Suk, Haejun Chung · 21 avril 2026

  Meta-optics promises compact, high-performance imaging and color routing. However, designing high-performance structures is a high-dimensional optimization problem: mapping a desired optical output back to a physical 3D structure requires solving computationally expensive Maxwell's equations iterati…

• Inverse Design of Optical Multilayer Thin Films using Robust Masked Diffusion Models

  Jonas Schaible, Asena Karolin \"Ozdemir, Charlotte Debus, Sven Burger, Achim Streit, Christiane Becker, Klaus J\"ager, Markus G\"otz · 2 avril 2026

  Inverse design of optical multilayer stacks seeks to infer layer materials, thicknesses, and ordering from a desired target spectrum. It is a long-standing challenge due to the large design space and non-unique solutions. We introduce \texttt{OptoLlama}, a masked diffusion language model for inverse…

• Neural Electromagnetic Fields for High-Resolution Material Parameter Reconstruction

  Zhe Chen, Peilin Zheng, Wenshuo Chen, Xiucheng Wang, Yutao Yue, Nan Cheng · 4 mars 2026

  Creating functional Digital Twins, simulatable 3D replicas of the real world, is a central challenge in computer vision. Current methods like NeRF produce visually rich but functionally incomplete twins. The key barrier is the lack of underlying material properties (e.g., permittivity, conductivity)…

• Fully-analog array signal processor using 3D aperture engineering

  Sheng Gao, Songtao Yang, Haiou Zhang, Yuan Shen, Xing Lin · 3 mars 2026

  The rapid progress in radar and communication places increasing demands on low-latency and energy-efficiency array signal processing methods. There is an emerging direction of constructing analog computing processors for directly processing electromagnetic (EM) waves. However, the existing methods a…

• Optimizing Spectral Prediction in MXene-Based Metasurfaces Through Multi-Channel Spectral Refinement and Savitzky-Golay Smoothing

  Shujaat Khan, Waleed Iqbal Waseer · 10 février 2026

  The prediction of electromagnetic spectra for MXene-based solar absorbers is a computationally intensive task, traditionally addressed using full-wave solvers. This study introduces an efficient deep learning framework incorporating transfer learning, multi-channel spectral refinement (MCSR), and Sa…

• Chat to Chip: Large Language Model Based Design of Arbitrarily Shaped Metasurfaces

  Huanshu Zhang, Lei Kang, Sawyer D. Campbell, Douglas H. Werner · 28 janvier 2026

  Traditional metasurface design is limited by the computational cost of full-wave simulations, preventing thorough exploration of complex configurations. Data-driven approaches have emerged as a solution to this bottleneck, replacing costly simulations with rapid neural network evaluations and enabli…

• Learned split-spectrum metalens for obstruction-free broadband imaging in the visible

  Seungwoo Yoon, Dohyun Kang, Eunsue Choi, Sohyun Lee, Seoyeon Kim, Minho Choi, Hyeonsu Heo, Dong-ha Shin, Suha Kwak, Arka Majumdar, Junsuk Rho, Seung-Hwan Baek · 28 janvier 2026

  Obstructions such as raindrops, fences, or dust degrade captured images, especially when mechanical cleaning is infeasible. Conventional solutions to obstructions rely on a bulky compound optics array or computational inpainting, which compromise compactness or fidelity. Metalenses composed of subwa…

• Meta-GPT: Decoding the Metasurface Genome with Generative Artificial Intelligence

  David Dang (Howard), Stuart Love (Howard), Meena Salib (Howard), Quynh Dang (Howard), Samuel Rothfarb (Howard), Mysk Alnatour (Howard), Andrew Salij (Howard), Hou-Tong Chen (Howard), Ho Wai (Howard), Lee, Wilton J. M. Kort-Kamp · 16 décembre 2025

  Advancing artificial intelligence for physical sciences requires representations that are both interpretable and compatible with the underlying laws of nature. We introduce METASTRINGS, a symbolic language for photonics that expresses nanostructures as textual sequences encoding materials, geometrie…

• MOCLIP: A Foundation Model for Large-Scale Nanophotonic Inverse Design

  S. Rodionov, A. Burguete-Lopez, M. Makarenko, Q. Wang, F. Getman, A. Fratalocchi · 25 novembre 2025

  Foundation models (FM) are transforming artificial intelligence by enabling generalizable, data-efficient solutions across different domains for a broad range of applications. However, the lack of large and diverse datasets limits the development of FM in nanophotonics. This work presents MOCLIP (Me…

• CoSP: Reconfigurable Multi-State Metamaterial Inverse Design via Contrastive Pretrained Large Language Model

  Shujie Yang, Xuzhe Zhao, Yuqi Zhang, Yansong Tang, Kaichen Dong · 21 novembre 2025

  Metamaterials, known for their ability to manipulate light at subwavelength scales, face significant design challenges due to their complex and sophisticated structures. Consequently, deep learning has emerged as a powerful tool to streamline their design process. Reconfigurable multi-state metamate…

• Diffusion-Based Electromagnetic Inverse Design of Scattering Structured Media

  Mikhail Tsukerman, Konstantin Grotov, Pavel Ginzburg · 10 novembre 2025

  We present a conditional diffusion model for electromagnetic inverse design that generates structured media geometries directly from target differential scattering cross-section profiles, bypassing expensive iterative optimization. Our 1D U-Net architecture with Feature-wise Linear Modulation learns…