Inverse design of optical lenses enabled by generative flow-based invertible neural networks

Developing an optical geometric lens system in a conventional way involves substantial effort from designers to devise and assess the lens specifications. An expeditious and effortless acquisition of lens parameters satisfying the desired lens performance requirements can ease the workload by avoidi...

Full description

Saved in:
Bibliographic Details
Published in:Scientific reports 2023-09, Vol.13 (1), p.16416-16416, Article 16416
Main Authors: Luo, Menglong, Lee, Sang-Shin
Format: Article
Language:English
Subjects:
639/624/1107
639/705/1042
639/705/117
Business metrics
Deep learning
Design
Humanities and Social Sciences
multidisciplinary
Neural networks
Optics
Optimization
Science
Science (multidisciplinary)
Software
Specifications
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Developing an optical geometric lens system in a conventional way involves substantial effort from designers to devise and assess the lens specifications. An expeditious and effortless acquisition of lens parameters satisfying the desired lens performance requirements can ease the workload by avoiding complex lens design process. In this study, we adopted the Glow, a generative flow model, which utilizes latent Gaussian variables to effectively tackle the issues of one-to-many mapping and information loss caused by dimensional disparities between high-dimensional lens structure parameters and low-dimensional performance metrics. We developed two lenses to tailor the vertical field of view and magnify the horizontal coverage range using two Glow-based invertible neural networks (INNs). By directly inputting the specified lens performance metrics into the proposed INNs, optimal inverse-designed lens specifications can be obtained efficiently with superb precision. The implementation of Glow-assisted INN approach is anticipated to significantly streamline the optical lens design workflows.
ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-023-43698-3