Tandem neural network-assisted inverse design of highly efficient diffractive slanted waveguide grating

Author:

Luo Menglong,Lee Sang-ShinORCID

Abstract

Virtual reality devices featuring diffractive grating components have emerged as hotspots in the field of near-to-eye displays. The core aim of our work is to streamline the intricacies involved in devising the highly efficient slanted waveguide grating using the deep-learning-driven inverse design technique. We propose and establish a tandem neural network (TNN) comprising a generative flow-based invertible neural network and a fully connected neural network. The proposed TNN can automatically optimize the coupling efficiencies of the proposed grating at multi-wavelengths, including red, green, and blue beams at incident angles in the range of 0°–15°. The efficiency indicators manifest in the peak transmittance, average transmittance, and illuminance uniformity, reaching approximately 100%, 92%, and 98%, respectively. Additionally, the structural parameters of the grating can be deduced inversely based on the indicators within a short duration of hundreds of milliseconds to seconds using the TNN. The implementation of the inverse-engineered grating is anticipated to serve as a paradigm for simplifying and expediting the development of diverse types of waveguide gratings.

Funder

Basic Science Research Program

National Research Foundation of Korea

Kwangwoon University

Ministry of Science and ICT, South Korea

Ministry of Education

Publisher

Optica Publishing Group

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