Fujita T, Sakaguchi H, Zhang J, Nonaka H, Sumi S, Awano H, Ishibashi T. Magneto-optical diffractive deep neural network.
OPTICS EXPRESS 2022;
30:36889-36899. [PMID:
36258609 DOI:
10.1364/oe.470513]
[Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/14/2022] [Accepted: 09/08/2022] [Indexed: 06/16/2023]
Abstract
We propose a magneto-optical diffractive deep neural network (MO-D2NN). We simulated several MO-D2NNs, 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-D2NN allows the hidden layers to be rewritten, which is not possible with previous implementations of D2NNs.
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