Photonic Neural Network Fabricated on Thin Film Lithium Niobate for High‐Fidelity and Power‐Efficient Matrix Computation

Abstract

Abstract 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, the first implementation of an EO tunable Mach‐Zehnder interferometer (MZI) mesh‐based TFLN PNN is presented. The device features ultra‐high fidelity, high computation speed, and exceptional power efficiency. The performance of the device is benchmarked with several deep learning missions including in situ training of Circle and Moons nonlinear datasets classification, Iris flower species recognition, and handwriting digits recognition. The work paves the way for sustainable up‐scaling of high‐speed, energy‐efficient PNNs.