铌酸锂
材料科学
功率(物理)
高保真
计算
薄膜
人工神经网络
光子学
忠诚
光电子学
基质(化学分析)
计算机科学
纳米技术
电信
电气工程
工程类
人工智能
物理
复合材料
算法
量子力学
作者
Yong Zheng,Rongbo Wu,Rong Yuan,Rui Bao,Jian Liu,Yue Ma,Min Wang,Y. Frank Cheng
出处
期刊:Cornell University - arXiv
日期:2024-02-26
标识
DOI:10.48550/arxiv.2402.16513
摘要
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.
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