Deep Neural Network Inverse Design of Integrated Nanophotonic Devices

Abstract

Predicting physical response of an artificially structured material is of particular interest for scientific and engineering applications. Here we use deep learning to predict optical response of artificially engineered nanophotonic devices. In addition to predicting forward approximation of transmission response for any given topology, this approach allows us to inversely approximate designs for a targeted optical response. Our Deep Neural Network (DNN) could design compact (2.6x2.6 μm2) silicon-on-insulator (SOI)-based 1 X 2 power splitters with various target splitting ratios in a fraction of a second. This model is trained to minimize the reflection (smaller than 20 dB) while achieving maximum transmission efficiency (above 90%) and target splitting specifications. This approach paves the way for rapid design of integrated photonic components relying on complex nanostructures.

References in corpus (9)

• Adam: A Method for Stochastic Optimization
• All-Optical Machine Learning Using Diffractive Deep Neural Networks
• Achieving transparency with plasmonic coatings
• Searching for Exotic Particles in High-Energy Physics with Deep Learning
• Training deep neural networks for the inverse design of nanophotonic structures
• Inverse design and demonstration of a compact and broadband on-chip wavelength demultiplexer
• Neuromorphic Silicon Photonic Networks
• Metasurface Broadband Solar Absorber
• Optimization of photonic crystal nanocavities based on deep learning

Cited by in corpus (6)

• Efficient spectrum prediction and inverse design for plasmonic waveguide systems based on artificial neural networks
• Deep Learning Reveals Underlying Physics of Light-matter Interactions in Nanophotonic Devices
• Designing Silicon Photonic Devices using Artificial Neural Networks
• Benchmarking deep inverse models over time, and the neural-adjoint method
• Probabilistic representation and inverse design of metamaterials based on a deep generative model with semi-supervised learning strategy
• High-Freedom Inverse Design with Deep Neural Network for Metasurface Filter in the Visible