Shaping freeform nanophotonic devices with geometric neural parameterization
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arXiv
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| Autori principali: | , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866908376267489280 |
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| author | Dai, Tianxiang Shao, Yixuan Mao, Chenkai Wu, Yu Azzouz, Sara Zhou, You Fan, Jonathan A. |
| author_facet | Dai, Tianxiang Shao, Yixuan Mao, Chenkai Wu, Yu Azzouz, Sara Zhou, You Fan, Jonathan A. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_17499 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Shaping freeform nanophotonic devices with geometric neural parameterization Dai, Tianxiang Shao, Yixuan Mao, Chenkai Wu, Yu Azzouz, Sara Zhou, You Fan, Jonathan A. Optics 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. |
| title | Shaping freeform nanophotonic devices with geometric neural parameterization |
| topic | Optics |
| url | https://arxiv.org/abs/2505.17499 |