The paper introduces a unified large language model workflow for modeling and inverse-design of metasurfaces across multiple families. By converting geometries, design parameters, and optical responses into a shared instruction‑following text format, the authors fine‑tune Gemma‑2‑9B on eight distinct metasurface families. Compared to single‑family models, the joint model predicts all families’ optical responses simultaneously and reduces mean‑squared error by an average of 56.5%.
"whyItMatters":"The approach demonstrates that a shared sequence‑based LLM interface can streamline cross‑family metasurface design, eliminating the need for separate surrogate architectures for each geometry class."
By Huanshu Zhang, Lei Kang, Yuyan Chen, Luxiang Wang, Zhaolong Cao, Douglas H. Werner
arXiv:2606. 00228v1 Announce Type: new Abstract: In semiconductor manufacturing, lithography projects circuit layouts onto silicon wafers through an optical mask.
By Yao Lai, Xuyuan Xiong, Zeyue Xue, Guojin Chen, Jing Wang, Xihui Liu, Rui Zhang, Robert Mullins, Bei Yu, Ping Luo
arXiv:2602. 08406v2 Announce Type: replace-cross Abstract: The prediction of electromagnetic spectra for MXene-based solar absorbers, where MXenes are a family of two-dimensional transition metal carbides and nitrides, is a computationally intensive task traditionally addressed using full-wave solvers.
By Shujaat Khan, Waleed Iqbal Waseer, Muhammad Shahid Jabbar
arXiv:2603. 15925v2 Announce Type: replace Abstract: Inverse design aims to find design parameters $x$ achieving target performance $y^*$.
By Miguel de Campos, Werner Krebs, Hanno Gottschalk
MOCLIP (Metasurface Optics Contrastive Learning Pretrained) is a nanophotonic foundation model that unifies metasurface geometry and spectra in a shared latent space using contrastive learning on an ImageNet‑1K–sized experimental dataset. It enables high‑throughput zero‑shot inverse design, predicting 0.2 million samples per second and allowing the design of a full 4‑inch wafer of high‑density metasurfaces in minutes. The model also supports generative latent‑space optimization with 97 % accuracy and demonstrates an optical information storage concept achieving 0.1 Gbit/mm², six times higher than commercial optical media.
By S. Rodionov, A. Burguete-Lopez, M. Makarenko, Q. Wang, F. Getman, A. Fratalocchi
arXiv:2608. 11860v1 Announce Type: cross Abstract: 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.
By Waleed Waseer, Muhammad Shahid Jabbar, Muhammad Sohail Ibrahim, Shujaat Khan
arXiv:2608. 14702v1 Announce Type: cross Abstract: Film emulation reproduces the look of an analog film stock on a new digital photograph.
By Yitong Mu
arXiv:2604. 02429v2 Announce Type: replace-cross Abstract: Convolutional neural networks (CNNs) have transformed image processing, but the energy consumption and inference latency of electronic based implementations remain fundamental bottlenecks.
By Saurabh Ranjan, Sonika Thakral, Amit Sehgal
The paper presents a collaborative on‑sensor array camera that uses a distributed meta‑optics learning method to jointly optimize a 100‑million‑nanopost metasurface array for broadband visible imaging. By training the array end‑to‑end with a learned meta‑atom proxy and a parallax‑aware, noise‑aware reconstruction algorithm, the design overcomes the wavelength‑dependent limitations of traditional metalenses. Experimental results show that the camera delivers consistent image quality across varying scene illumination spectra without relying on generative reconstruction.
By Jipeng Sun, Kaixuan Wei, Thomas Eboli, Congli Wang, Cheng Zheng, Zhihao Zhou, Arka Majumdar, Wolfgang Heidrich, Felix Heide
arXiv:2608. 04043v1 Announce Type: new Abstract: Resistive pressure arrays are the cheapest and most widely shipped tactile sensors, yet tactile representation learning has concentrated on optical sensors that image a deforming gel.
By Abdul Basit Tonmoy
The review examines how Large Language Models (LLMs) enhance nanophotonics design by providing semantic interfaces, code generation, and tool orchestration. It traces the evolution from classical neural networks to transformer-based models and categorizes LLM applications into surrogate models that map structure to spectrum and agentic systems that generate code and orchestrate simulations for closed-loop optimization. The article also highlights potential cross-disciplinary uses of LLMs in materials science and wireless communications, and envisions future multimodal foundation models that actively collaborate in autonomous scientific discovery.
By Huanshu Zhang, Kegeng Tang, Lei Kang, Sawyer D. Campbell, Zihao Wang, Douglas H. Werner
Metasurfaces have revolutionized the development of photonic devices by enabling unprecedented precision in light manipulation. However, their design processes are often constrained by computationally...