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arXiv · 2505.17499

Shaping freeform nanophotonic devices with geometric neural parameterization

Tianxiang Dai·Yixuan Shao·Chenkai Mao·Yu Wu·Sara Azzouz·You Zhou·Jonathan A. Fan
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Abstract

Nanophotonic freeform design has the potential to push the performance of optical components to new limits, but there remains a challenge to effectively perform optimization while reliably enforcing design and manufacturing constraints. We present Neuroshaper, a framework for freeform geometric parameterization in which nanophotonic device layouts are defined using an analytic neural network representation. Neuroshaper serves as a qualitatively new way to perform shape optimization by capturing multi-scalar, freeform geometries in an overparameterized representation scheme, enabling effective optimization in a smoothened, high dimensional geometric design space. We show that Neuroshaper can enforce constraints and topology manipulation in a manner where local constraints lead to global changes in device morphology. We further show numerically and experimentally that Neuroshaper can apply to a diversity of nanophotonic devices. The versatility and capabilities of Neuroshaper reflect the ability of neural representation to augment concepts in topological design.

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Tianxiang Dai, Yixuan Shao, Chenkai Mao, Yu Wu, Sara Azzouz, You Zhou, Jonathan A. Fan. 2025-05-23. Shaping freeform nanophotonic devices with geometric neural parameterization. https://arxiv.org/abs/2505.17499

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