Compact All optical Reservoir Computing via Luminescence Dynamics in Rare-earth Ions-doped Nanocrystals
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866908498766331904 |
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| author | Chen, Junyan Fu, Jingsong Xu, Jie Qin, Yixiang Du, Axin Wang, Kaiyang Jin, Limin Huang, Can |
| author_facet | Chen, Junyan Fu, Jingsong Xu, Jie Qin, Yixiang Du, Axin Wang, Kaiyang Jin, Limin Huang, Can |
| contents | Optical neuromorphic computing offers a promising route to high speed, energy efficient information processing. However, photonic neurons, as the critical components for enhancing computational expressivity, still face significant bottlenecks in nonlinear mapping and memory capacity. Here, we demonstrate an all optical reservoir computing system based on rare earth ions doped nanocrystals for the first time, leveraging their intrinsic nonlinear luminescence dynamics and multitimescale memory. Unlike traditional schemes that require bulky optical delays or intricate resonant structures, our platform exploits the material's inherent properties: nonlinear cross-relaxation processes enable nonlinear mapping while millisecond-scale metastable energy levels provide fading memory. As a proof of concept, we achieve 90.7% accuracy in MNIST digit classification and low-error chaotic time-series prediction (NRMSE < 0.1) using the rare-earth ions based system. Our work significantly reduce system footprint and complexity, offering a scalable, fully optical solution for edge computing and real-time neuromorphic applications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_16042 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Compact All optical Reservoir Computing via Luminescence Dynamics in Rare-earth Ions-doped Nanocrystals Chen, Junyan Fu, Jingsong Xu, Jie Qin, Yixiang Du, Axin Wang, Kaiyang Jin, Limin Huang, Can Optics Optical neuromorphic computing offers a promising route to high speed, energy efficient information processing. However, photonic neurons, as the critical components for enhancing computational expressivity, still face significant bottlenecks in nonlinear mapping and memory capacity. Here, we demonstrate an all optical reservoir computing system based on rare earth ions doped nanocrystals for the first time, leveraging their intrinsic nonlinear luminescence dynamics and multitimescale memory. Unlike traditional schemes that require bulky optical delays or intricate resonant structures, our platform exploits the material's inherent properties: nonlinear cross-relaxation processes enable nonlinear mapping while millisecond-scale metastable energy levels provide fading memory. As a proof of concept, we achieve 90.7% accuracy in MNIST digit classification and low-error chaotic time-series prediction (NRMSE < 0.1) using the rare-earth ions based system. Our work significantly reduce system footprint and complexity, offering a scalable, fully optical solution for edge computing and real-time neuromorphic applications. |
| title | Compact All optical Reservoir Computing via Luminescence Dynamics in Rare-earth Ions-doped Nanocrystals |
| topic | Optics |
| url | https://arxiv.org/abs/2508.16042 |