Photonic Neural Network Fabricated on Thin Film Lithium Niobate for High-Fidelity and Power-Efficient Matrix Computation
<|reference_start|>Photonic Neural Network Fabricated on Thin Film Lithium Niobate for High-Fidelity and Power-Efficient Matrix Computation: 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.<|reference_end|>
@article{zheng2024photonic,
    title={Photonic Neural Network Fabricated on Thin Film Lithium Niobate for
  High-Fidelity and Power-Efficient Matrix Computation},
    author={Yong Zheng, Rongbo Wu, Yuan Ren, Rui Bao, Jian Liu, Yu Ma, Min Wang
  and Ya Cheng},
    journal={arXiv preprint arXiv:2402.16513},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.16513},
    primaryClass={physics.optics cs.ET physics.app-ph}
}