Wavelength Controllable Forward Prediction and Inverse Design of Nanophotonic Devices Using Deep Learning

TL;DR

This paper introduces a deep learning model that enables wavelength-controllable forward prediction and inverse design of nanophotonic devices, allowing for flexible and efficient implementation of functions like power splitters and wavelength demultiplexers.

Contribution

It presents a novel deep learning framework that simultaneously utilizes time-domain and wavelength-domain data for nanophotonic device design.

Findings

Enables flexible design of nanophotonic devices.

Achieves efficient wavelength control in device prediction.

Supports multiple device functions like power splitting and demultiplexing.

Abstract

A deep learning-based wavelength controllable forward prediction and inverse design model of nanophotonic devices is proposed. Both the target time-domain and wavelength-domain information can be utilized simultaneously, which enables multiple functions, including power splitter and wavelength demultiplexer, to be implemented efficiently and flexibly.