Photonic Neural Network Fabricated on Thin Film Lithium Niobate for High-Fidelity and Power-Efficient Matrix Computation
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866910343920353280 |
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| author | Zheng, Yong Wu, Rongbo Ren, Yuan Bao, Rui Liu, Jian Ma, Yu Wang, Min Cheng, Ya |
| author_facet | Zheng, Yong Wu, Rongbo Ren, Yuan Bao, Rui Liu, Jian Ma, Yu Wang, Min Cheng, Ya |
| contents | Photonic neural networks (PNNs) have emerged as a promising platform to address the energy consumption issue that comes with the advancement of artificial intelligence technology, and thin film lithium niobate (TFLN) offers an attractive solution as a material platform mainly for its combined characteristics of low optical loss and large electro-optic (EO) coefficients. Here, we present the first implementation of an EO tunable PNN based on the TFLN platform. Our device features ultra-high fidelity, high computation speed, and exceptional power efficiency. We benchmark the performance of our device with several deep learning missions including in-situ training of Circle and Moons nonlinear datasets classification, Iris flower species recognition, and handwriting digits recognition. Our work paves the way for sustainable up-scaling of high-speed, energy-efficient PNNs. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_16513 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Photonic Neural Network Fabricated on Thin Film Lithium Niobate for High-Fidelity and Power-Efficient Matrix Computation Zheng, Yong Wu, Rongbo Ren, Yuan Bao, Rui Liu, Jian Ma, Yu Wang, Min Cheng, Ya Optics Emerging Technologies Applied Physics Photonic neural networks (PNNs) have emerged as a promising platform to address the energy consumption issue that comes with the advancement of artificial intelligence technology, and thin film lithium niobate (TFLN) offers an attractive solution as a material platform mainly for its combined characteristics of low optical loss and large electro-optic (EO) coefficients. Here, we present the first implementation of an EO tunable PNN based on the TFLN platform. Our device features ultra-high fidelity, high computation speed, and exceptional power efficiency. We benchmark the performance of our device with several deep learning missions including in-situ training of Circle and Moons nonlinear datasets classification, Iris flower species recognition, and handwriting digits recognition. Our work paves the way for sustainable up-scaling of high-speed, energy-efficient PNNs. |
| title | Photonic Neural Network Fabricated on Thin Film Lithium Niobate for High-Fidelity and Power-Efficient Matrix Computation |
| topic | Optics Emerging Technologies Applied Physics |
| url | https://arxiv.org/abs/2402.16513 |