Tianxiang Dai
Ph.D. Student in Electrical Engineering, admitted Autumn 2023
All Publications
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Freeform Mode-Engineered Metasurfaces.
Nano letters
2026
Abstract
Nanophotonic technologies inherently rely on tailoring light-matter interactions through the excitation and interference of deeply confined optical resonances. However, existing concepts in optical mode engineering remain heuristic and are challenging to extend toward complex and multifunctional resonant phenomena. We introduce an inverse design framework that optimizes near-field distributions, ideally suited to tailoring Mie-type modes within dielectric nanophotonic structures, and we demonstrate its application to the discovery of new classes of nonlocal metasurfaces. We show that freeform nonlocal metasurfaces supporting accidental bound states in the continuum can be readily optimized for tailored illumination conditions, modal properties, and quality factors. We further generalize the framework to higher-order and multifunctional mode engineering and experimentally demonstrate freeform planar nonlocal multiwavelength and chiral metasurfaces. Our versatile framework for freeform mode engineering has applications in broad high-quality-factor nanophotonic platforms relevant to sensing, nonlinear optics, optomechanics, and quantum information processing.
View details for DOI 10.1021/acs.nanolett.5c06075
View details for PubMedID 41805361
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Conformal Aberration-Correcting Spaceplates
LASER & PHOTONICS REVIEWS
2026
View details for DOI 10.1002/lpor.202502414
View details for Web of Science ID 001653747400001
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A multi-agentic framework for real-time, autonomous freeform metasurface design.
Science advances
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
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Shaping freeform nanophotonic devices with geometric neural parameterization
NPJ COMPUTATIONAL MATERIALS
2025; 11 (1)
View details for DOI 10.1038/s41524-025-01752-w
View details for Web of Science ID 001546112400001
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Hetero-integrated perovskite/Si<sub>3</sub>N<sub>4</sub> on-chip photonic system
NATURE PHOTONICS
2025
View details for DOI 10.1038/s41566-024-01603-y
View details for Web of Science ID 001388182900001
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Ultracompact and multifunctional integrated photonic platform.
Science advances
2024; 10 (25): eadm7569
Abstract
Realizing a multifunctional integrated photonic platform is one of the goals for future optical information processing, which usually requires large size to realize due to multiple integration challenges. Here, we realize a multifunctional integrated photonic platform with ultracompact footprint based on inverse design. The photonic platform is compact with 86 inverse designed-fixed couplers and 91 phase shifters. The footprint of each coupler is 4 mum by 2 mum, while the whole photonic platform is 3 mm by 0.2 mm-one order of magnitude smaller than previous designs. One-dimensional Floquet Su-Schrieffer-Heeger model and Aubry-Andre-Harper model are performed with measured fidelities of 97.90 (±0.52) % and 99.34 (±0.44) %, respectively. We also demonstrate a handwritten digits classification task with the test accuracy of 87% using on-chip training. Moreover, the scalability of this platform has been proved by demonstrating more complex computing tasks. This work provides an effective method to realize an ultrasmall integrated photonic platform.
View details for DOI 10.1126/sciadv.adm7569
View details for PubMedID 38896615
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Graphene/silicon heterojunction for reconfigurable phase-relevant activation function in coherent optical neural networks.
Nature communications
2023; 14 (1): 6939
Abstract
Optical neural networks (ONNs) herald a new era in information and communication technologies and have implemented various intelligent applications. In an ONN, the activation function (AF) is a crucial component determining the network performances and on-chip AF devices are still in development. Here, we first demonstrate on-chip reconfigurable AF devices with phase activation fulfilled by dual-functional graphene/silicon (Gra/Si) heterojunctions. With optical modulation and detection in one device, time delays are shorter, energy consumption is lower, reconfigurability is higher and the device footprint is smaller than other on-chip AF strategies. The experimental modulation voltage (power) of our Gra/Si heterojunction achieves as low as 1V (0.5mW), superior to many pure silicon counterparts. In the photodetection aspect, a high responsivity of over 200mA/W is realized. Special nonlinear functions generated are fed into a complex-valued ONN to challenge handwritten letters and image recognition tasks, showing improved accuracy and potential of high-efficient, all-component-integration on-chip ONN. Our results offer new insights for on-chip ONN devices and pave the way to high-performance integrated optoelectronic computing circuits.
View details for DOI 10.1038/s41467-023-42116-6
View details for PubMedID 37907477
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Integrated Photonic Neural Networks: Opportunities and
ACS PHOTONICS
2023; 10 (7): 2001-2010
View details for DOI 10.1021/acsphotonics.2c01516
View details for Web of Science ID 000963349400001
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Snapshot Mueller spectropolarimeter imager.
Microsystems & nanoengineering
2023; 9: 125
Abstract
We introduce an imaging system that can simultaneously record complete Mueller polarization responses for a set of wavelength channels in a single image capture. The division-of-focal-plane concept combines a multiplexed illumination scheme based on Fourier optics together with an integrated telescopic light-field imaging system. Polarization-resolved imaging is achieved using broadband nanostructured plasmonic polarizers as functional pinhole apertures. The recording of polarization and wavelength information on the image sensor is highly interpretable. We also develop a calibration approach based on a customized neural network architecture that can produce calibrated measurements in real-time. As a proof-of-concept demonstration, we use our calibrated system to accurately reconstruct a thin film thickness map from a four-inch wafer. We anticipate that our concept will have utility in metrology, machine vision, computational imaging, and optical computing platforms.
View details for DOI 10.1038/s41378-023-00588-y
View details for PubMedID 37814609
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Matrix eigenvalue solver based on reconfigurable photonic neural network
NANOPHOTONICS
2022; 11 (17): 4089-4099
View details for DOI 10.1515/nanoph-2022-0109
View details for Web of Science ID 000787158300001
https://orcid.org/0000-0002-9403-7511