Ensemble nonlinear optical learner by electrically tunable linear scattering
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arXiv
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| Auteurs principaux: | , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866915358145773568 |
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| author | Xia, Tunan Wu, Cheng-Kuan Guo, Duan-Yi Zhang, Lidan Liu, Bofeng Lin, Tsung-Hsien Ni, Xingjie Khoo, Iam-Choon Liu, Zhiwen |
| author_facet | Xia, Tunan Wu, Cheng-Kuan Guo, Duan-Yi Zhang, Lidan Liu, Bofeng Lin, Tsung-Hsien Ni, Xingjie Khoo, Iam-Choon Liu, Zhiwen |
| contents | Recent progress in effective nonlinearity, achieved by exploiting multiple scatterings within the linear optical regime, has been demonstrated to be a promising approach to enable nonlinear optical processing without relying on actual material nonlinearity. Here we introduce an ensemble nonlinear optical learner, via electrically tunable linear scattering in a liquid-crystal-polymer composite film under low optical power and low applied electrical voltages. We demonstrate, through several image classification tasks, that by combining inference results from an ensemble of nonlinear optical learners realized at different applied voltages, the ensemble optical learning significantly outperforms the classification performance of individual processors. With very low-level optical power and electrical voltage requirements, and ease in reconfiguration simply by varying applied voltages, the ensemble nonlinear optical learning offers a cost-effective and flexible way to improve computing performance and enhance inference accuracy. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_19976 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Ensemble nonlinear optical learner by electrically tunable linear scattering Xia, Tunan Wu, Cheng-Kuan Guo, Duan-Yi Zhang, Lidan Liu, Bofeng Lin, Tsung-Hsien Ni, Xingjie Khoo, Iam-Choon Liu, Zhiwen Optics Disordered Systems and Neural Networks Recent progress in effective nonlinearity, achieved by exploiting multiple scatterings within the linear optical regime, has been demonstrated to be a promising approach to enable nonlinear optical processing without relying on actual material nonlinearity. Here we introduce an ensemble nonlinear optical learner, via electrically tunable linear scattering in a liquid-crystal-polymer composite film under low optical power and low applied electrical voltages. We demonstrate, through several image classification tasks, that by combining inference results from an ensemble of nonlinear optical learners realized at different applied voltages, the ensemble optical learning significantly outperforms the classification performance of individual processors. With very low-level optical power and electrical voltage requirements, and ease in reconfiguration simply by varying applied voltages, the ensemble nonlinear optical learning offers a cost-effective and flexible way to improve computing performance and enhance inference accuracy. |
| title | Ensemble nonlinear optical learner by electrically tunable linear scattering |
| topic | Optics Disordered Systems and Neural Networks |
| url | https://arxiv.org/abs/2506.19976 |