In conventional optical microscopes, image contrast of objects mainly results from the differences in light intensity and/or color. Muller matrix optical microscopes (MMMs), on the other hand, can provide significantly enhanced image contrast and rich information about objects by analyzing their interactions with polarized light. However, state‐of‐the‐art MMMs are fundamentally limited by bulky and slow polarization state generators and analyzers. Here, the study demonstrates a metasurface‐based MMM, i.e., Meta‐MMM, which is equipped with a chip‐integrated, single‐shot metasurface polarization state analyzer (Meta‐PSA). The Meta‐MMM is featured with high‐speed measurement (≈2s per Muller matrix (MM) image), superior operation stability, dual‐color operation, and high measurement accuracy (measurement error 1–2%) for MM imaging. The Meta‐MMM is applied to nanostructure characterization, surface morphology analysis, and discovering birefringent structures in honeybee wings. The Meta‐MMMs hold the promise to revolutionize various applications from biological imaging, medical diagnosis, and material characterization to industry inspection and space exploration.
Multifunctional metamaterials (MMM) bear promise as next‐generation material platforms supporting miniaturization and customization. Despite many proof‐of‐concept demonstrations and the proliferation of deep learning assisted design, grand challenges of inverse design for MMM, especially those involving heterogeneous fields possibly subject to either mutual meta‐atom coupling or long‐range interactions, remain largely under‐explored. To this end, a data‐driven design framework is presented, which streamlines the inverse design of MMMs involving heterogeneous fields. A core enabler is implicit Fourier neural operator (IFNO), which predicts heterogeneous fields distributed across a metamaterial array, thus in general at odds with homogenization assumptions. Additionally, a standard formulation of inverse problem covering a broad class of MMMs is presented, together with gradient‐based multitask concurrent optimization identifying a set of Pareto‐optimal architecture‐stimulus (A‐S) pairs. Fourier multiclass blending is proposed to synthesize inter‐class meta‐atoms anchored on a set of geometric motifs, while enjoying training‐free dimension reduction and built‐it reconstruction. Interlocking the three pillars, the framework is validated for light‐by‐light programmable nanoantenna, whose design involves vast space jointly spanned by quasi‐freeform supercells, maneuverable incident phase distributions, and conflicting figure‐of‐merits (FoM) involving on‐demand localization patterns. Accommodating all the challenges, the framework can propel future advancements of MMM.
more » « less- Award ID(s):
- 1753031
- NSF-PAR ID:
- 10497283
- Publisher / Repository:
- Wiley Blackwell (John Wiley & Sons)
- Date Published:
- Journal Name:
- Advanced Optical Materials
- Volume:
- 12
- Issue:
- 15
- ISSN:
- 2195-1071
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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