Chenkai Mao

All Publications

• 3D nanolithography with metalens arrays and spatially adaptive illumination. Nature Gu, S., Mao, C., Guell Izard, A., Sadana, S., Terrel-Perez, D., Mettry-Yassa, M., Choi, W., Zhou, W., Yan, H., Zhou, Z., Massey, T., Abelson, A., Zhou, Y., Huang, S., Daraio, C., Tumkur, T. U., Fan, J. A., Xia, X. 2025; 648 (8094): 591-599

  Abstract

  The growing demand for advanced materials, miniaturized devices and integrated microsystems calls for the reliable fabrication of complex, multiscale, three-dimensional (3D) architectures, a need increasingly addressed through light-based and laser-based processes. However, owing to the field-of-view (FOV) limitations of conventional imaging optics, existing 3D laser nanofabrication techniques face fundamental challenges in throughput, proximity error and stitching defects on the path to scaling. Here we present a scalable 3D nanofabrication platform that uses a metalens-generated focal spot array to parallelize two-photon lithography (TPL)1 beyond centimetre-scale write field areas. Metalenses are ideally suited for producing submicron-scale focal spots for high-throughput nanolithography, as they uniquely feature large numerical apertures (NAs), immersion media compatibility and large-scale manufacturability. We experimentally demonstrate a printing system that uses a 12-cm2 metalens array to produce more than 120,000 cooperative focal spots, corresponding to a throughput exceeding 108 voxels s-1. By programmatically patterning the focal spot array using a spatial light modulator (SLM), an adaptive parallel printing strategy is developed for precise greyscale linewidth modulation and choreographed printing of semiperiodic and fully aperiodic 3D geometries. We demonstrate parallel printing of replicated microstructures (>50 M microparticles per day), centimetre-scale 3D architectures with feature sizes down to 113 nm, and photonic and mechanical metamaterials. This work demonstrates the potential of 3D nanolithography towards wafer-scale production, showing how TPL could be used at scale for applications in microelectronics2, biomedicine3, quantum technology4 and high-energy laser targets5,6.

  View details for DOI 10.1038/s41586-025-09842-x

  View details for PubMedID 41407898

  View details for PubMedCentralID 11525192

• A multi-agentic framework for real-time, autonomous freeform metasurface design. Science advances Lupoiu, R., Shao, Y., Dai, T., Mao, C., Edée, K., Fan, J. A. 2025; 11 (44): eadx8006

  Abstract

  Innovation in nanophotonics currently relies on human experts who synergize specialized knowledge in photonics and coding with simulation and optimization algorithms, entailing design cycles that are time-consuming, computationally demanding, and frequently suboptimal. We introduce MetaChat, a multi-agentic design framework that can translate semantically described photonic design goals into high-performance, freeform device layouts in an automated, nearly real-time manner. Multistep reasoning is enabled by our Agentic Iterative Monologue paradigm, which coherently interfaces agents with code-based tools, other specialized agents, and human designers. Design acceleration is facilitated by Feature-wise Linear Modulation-conditioned Maxwell surrogate solvers that support the generalized evaluation of metasurface structures. We use freeform dielectric metasurfaces as a model system and demonstrate with MetaChat the design of multiobjective, multiwavelength metasurfaces orders of magnitude faster than conventional methods. These concepts present a scientific computing blueprint for using specialist design agents, surrogate solvers, and human interactions to drive multiphysics innovation and discovery.

  View details for DOI 10.1126/sciadv.adx8006

  View details for PubMedID 41171904

• Shaping freeform nanophotonic devices with geometric neural parameterization NPJ COMPUTATIONAL MATERIALS Dai, T., Shao, Y., Mao, C., Wu, Y., Azzouz, S., Zhou, Y., Fan, J. A. 2025; 11 (1)

  View details for DOI 10.1038/s41524-025-01752-w

  View details for Web of Science ID 001546112400001

• Inverse-designed metasurfaces with facile fabrication parameters JOURNAL OF OPTICS Zhou, Y., Shao, Y., Mao, C., Fan, J. A. 2024; 26 (5)

  View details for DOI 10.1088/2040-8986/ad33a7

  View details for Web of Science ID 001188548100001

• Large-Area, High-Numerical-Aperture, Freeform Metasurfaces LASER & PHOTONICS REVIEWS Zhou, Y., Mao, C., Gershnabel, E., Chen, M., Fan, J. A. 2024

  View details for DOI 10.1002/lpor.202300988

  View details for Web of Science ID 001163678600001

• Towards General Neural Surrogate Solvers with Specialized Neural Accelerators Proceedings of the 41st International Conference on Machine Learning Mao, C., Lupoiu, R., Dai, T., Chen, M., Fan, J. 2024
• Reparameterization Approach to Gradient-Based Inverse Design of Three-Dimensional Nanophotonic Devices ACS PHOTONICS Gershnabel, E., Chen, M., Mao, C., Wang, E. W., Lalanne, P., Fan, J. A. 2022

  View details for DOI 10.1021/acsphotonics.2c01160

  View details for Web of Science ID 000874561200001

• High Speed Simulation and Freeform Optimization of Nanophotonic Devices with Physics-Augmented Deep Learning ACS PHOTONICS Chen, M., Lupoiu, R., Mao, C., Huang, D., Jiang, J., Lalanne, P., Fan, J. A. 2022

  View details for DOI 10.1021/acsphotonics.2c00876

  View details for Web of Science ID 000848111900001

• WaveY-Net: Physics-Augmented Deep Learning for High-Speed Electromagnetic Simulation and Optimization Chen, M., Lupoiu, R., Mao, C., Huang, D., Jiang, J., Lalanne, P., Fan, J. A. edited by Chang-Hasnain, C. J., Fan, J. A., Zhou, W. SPIE-INT SOC OPTICAL ENGINEERING. 2022

  View details for DOI 10.1117/12.2612418

  View details for Web of Science ID 000836330700011