REVIEW
Wavelength Controllable Forward Prediction and Inverse Design of Nanophotonic Devices Using Deep Learning
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
Wavelength Controllable Forward Prediction and Inverse Design of Nanophotonic Devices Using Deep Learning
classification
physics.optics
keywords
wavelengthcontrollabledeepdesigndevicesforwardinversenanophotonic
read the original 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.
discussion (0)
Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.