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Magneto- optical diffractive deep neural network
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DOI：10.1364/OE.470513.png)
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Abstract
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We propose a magneto-optical diffractive deep neural network (MO-(DNN)-N-2). We simulated several MO-D(2)NNs, each of which consists of five hidden layers made of a magnetic material that contains 100 x 100 magnetic domains with a domain width of 1 mu m 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 p/100 rad when the angle of polarization is used as the classification measure. The MO-(DNN)-N-2 allows the hidden layers to be rewritten, which is not possible with previous implementations of D(2)NNs.
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