Hetero-integrated perovskite/Si3N4 on-chip photonic system

Abstract

Integrated photonic chips hold substantial potential in optical communications, computing, light detection and ranging, sensing, and imaging, offering exceptional data throughput and low power consumption. A key objective is to build a monolithic on-chip photonic system that integrates light sources, processors and photodetectors on a single chip. However, this remains challenging due to limitations in materials engineering, chip integration techniques and design methods. Perovskites offer simple fabrication, tolerance to lattice mismatch, flexible bandgap tunability and low cost, making them promising for hetero-integration with silicon photonics. Here we propose and experimentally realize a near-infrared monolithic on-chip photonic system based on a perovskite/silicon nitride photonic platform, developing nano-hetero-integration technology to integrate efficient light-emitting diodes, high-performance processors and sensitive photodetectors. Photonic neural networks are implemented to perform photonic simulations and computer vision tasks. Our network efficiently predicts the topological invariant in a two-dimensional disordered Su–Schrieffer–Heeger model and simulates nonlinear topological models with an average fidelity of 87%. In addition, we achieve a test accuracy of over 85% in edge detection and 56% on the CIFAR-10 dataset using a scaled-up architecture. This work addresses the challenge of integrating diverse nanophotonic components on a chip, offering a promising solution for chip-integrated multifunctional photonic information processing.