Photonic Neuromorphic Computing enabled by a BIC Metasurface
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arXiv
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| Main Authors: | , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866914352252059648 |
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| author | Fu, Jingsong Jin, Ruiheng Xie, Zhaohui Tang, Haijun Jiang, Xiong Cui, Yue Kong, Xiangtong Hao, Wentao Qu, Geyang Huang, Can Song, Qingha |
| author_facet | Fu, Jingsong Jin, Ruiheng Xie, Zhaohui Tang, Haijun Jiang, Xiong Cui, Yue Kong, Xiangtong Hao, Wentao Qu, Geyang Huang, Can Song, Qingha |
| contents | Photonic neuromorphic computing promises revolutionary advances in parallel and high-speed processing, yet a key challenge persists: co-integrating nonlinearity, dense connectivity, and intrinsic memory monolithically to enable brain-inspired, spatiotemporal information processing. Here, we overcome this challenge by introducing a monolithic photonic recurrent network based on an active metasurface operating at bound state in the continuum (BIC). The BIC mode mediates strong,long-range coupling across the lattice, creating a reconfigurable recurrent network topology in hardware. Concurrently, the gain medium provides both optical nonlinearity for neuronal activation and a finite carrier lifetime that serves as a built in, analog temporal memory. This synergy enables computation to emerge directly from the collective spatiotemporal dynamics of the driven-dissipative photonic system, effectively realizing a physical reservoir computer on a chip. We experimentally validate a minimal yet physically complete system on benchmark tasks: brain MRI image classification and human action recognition, achieving 92.16% and 85.36% accuracies, respectively. This work establishes a scalable pathway toward ultrafast, energy-efficient neuromorphic intelligence where processing is an inherent property of tailored light matter interaction. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_22528 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Photonic Neuromorphic Computing enabled by a BIC Metasurface Fu, Jingsong Jin, Ruiheng Xie, Zhaohui Tang, Haijun Jiang, Xiong Cui, Yue Kong, Xiangtong Hao, Wentao Qu, Geyang Huang, Can Song, Qingha Optics Photonic neuromorphic computing promises revolutionary advances in parallel and high-speed processing, yet a key challenge persists: co-integrating nonlinearity, dense connectivity, and intrinsic memory monolithically to enable brain-inspired, spatiotemporal information processing. Here, we overcome this challenge by introducing a monolithic photonic recurrent network based on an active metasurface operating at bound state in the continuum (BIC). The BIC mode mediates strong,long-range coupling across the lattice, creating a reconfigurable recurrent network topology in hardware. Concurrently, the gain medium provides both optical nonlinearity for neuronal activation and a finite carrier lifetime that serves as a built in, analog temporal memory. This synergy enables computation to emerge directly from the collective spatiotemporal dynamics of the driven-dissipative photonic system, effectively realizing a physical reservoir computer on a chip. We experimentally validate a minimal yet physically complete system on benchmark tasks: brain MRI image classification and human action recognition, achieving 92.16% and 85.36% accuracies, respectively. This work establishes a scalable pathway toward ultrafast, energy-efficient neuromorphic intelligence where processing is an inherent property of tailored light matter interaction. |
| title | Photonic Neuromorphic Computing enabled by a BIC Metasurface |
| topic | Optics |
| url | https://arxiv.org/abs/2602.22528 |