Deep Photonic Networks with Arbitrary and Broadband Functionality
arXiv:2304.04776 · doi:10.1038/s41467-024-45846-3
Abstract
Growing application space in optical communications, computing, and sensing continues to drive the need for high-performance integrated photonic components. Designing these on-chip systems with complex and application-specific functionality requires beyond what is possible with physical intuition, for which machine learning-based design methods have recently become popular. However, as the expensive computational requirements for physically accurate device simulations last a critical challenge, these methods typically remain limited in scalability and the optical design degrees of freedom they can provide for application-specific and arbitrary photonic integrated circuits. Here, we introduce a highly-scalable, physics-informed framework for the design of on-chip optical systems with arbitrary functionality based on a deep photonic network of custom-designed Mach-Zehnder interferometers. Using this framework, we design ultra-broadband power splitters and a spectral duplexer, each in less than two minutes, and demonstrate state-of-the-art experimental performance with less than 0.66 dB insertion loss and over 120 nm of 1-dB bandwidth for all devices. Our presented framework provides an essential tool with a tractable path towards the systematic design of large-scale photonic systems with custom and broadband power, phase, and dispersion profiles for use in multi-band optical applications including high-throughput communications, quantum information processing, and medical/biological sensing.
References in corpus (13)
- Deep Learning with Coherent Nanophotonic Circuits
- All-Optical Machine Learning Using Diffractive Deep Neural Networks
- Parallel convolution processing using an integrated photonic tensor core
- Outlook for inverse design in nanophotonics
- Photonics for artificial intelligence and neuromorphic computing
- Inverse design and demonstration of a compact and broadband on-chip wavelength demultiplexer
- Universal Linear Optics
- Single-chip photonic deep neural network for instantaneous image classification
- Quantum transport simulations in a programmable nanophotonic processor
- Deep learning in nano-photonics: inverse design and beyond
- Self-Configuring Universal Linear Optical Component
- Nanophotonic Computational Design
- All-optical mode unscrambling on a silicon photonic chip