Open AccessMagneto-optical diffractive deep neural network
Author(s) -
Takumi Fujita,
Hotaka Sakaguchi,
Jian Zhang,
Hirofumi aka,
Shoichiro Sumi,
Hiroyuki Awano,
Takayuki Ishibashi
Publication year - 2022
Publication title -
optics express
Language(s) - Uncategorized
Resource type - Journals
SCImago Journal Rank - 1.394
H-Index - 271
ISSN - 1094-4087
DOI - 10.1364/oe.470513
Subject(s) - faraday effect , optics , artificial neural network , mnist database , polarization (electrochemistry) , measure (data warehouse) , physics , magneto optical , faraday cage , artificial intelligence , computer science , magnetic field , chemistry , quantum mechanics , database
We propose a magneto-optical diffractive deep neural network (MO-D 2 NN). We simulated several MO-D 2 NNs, each of which consists of five hidden layers made of a magnetic material that contains 100 × 100 magnetic domains with a domain width of 1 µm and an interlayer distance of 0.7 mm. The networks demonstrate a classification accuracy of > 90% for the MNIST dataset when light intensity is used as the classification measure. Moreover, an accuracy of > 80% is obtained even for a small Faraday rotation angle of π/100 rad when the angle of polarization is used as the classification measure. The MO-D 2 NN allows the hidden layers to be rewritten, which is not possible with previous implementations of D 2 NNs.