Development of Fabrication Techniques for Magneto-Optical Diffractive Deep Neural Networks

Hotaka Sakaguchi,Takumi Fujita,Hiroyuki Awano,Takayuki Ishibashi,Satoshi Sumi,Jian Zhang,Hirofumi Nonaka

doi:10.1109/tmag.2023.3281842

Abstract

Magneto-optical diffractive deep neural network (MO-D 2 NN) is a type of diffractive deep neural network that utilizes the magneto-optical effect of magnetic materials. MO-D 2 NN is composed of magnetic material, which is non-volatile and allows neurons to be rewritten. In our work, we fabricated and evaluated MO-D 2 NN with 2 layers containing 100 × 100 magnetic domains with a domain size of 1 μm. Nd 0.5 Bi 2.5 Fe 4 GaO 12 thin films were prepared on the both sides of a Gd 3 Ga 5 O 12 substrate and magnetic domain patterns were recorded using the magneto-optical recording technique. The recording accuracies of first and second layer were 79% and 70%, respectively. We believed that magneto-optical recording technology can achieve a recording accuracy of over 90% by optimizing the fabrication and recording conditions. The optical setup was built and handwritten digit classification was carried out using fabricated MO-D 2 NN, but the number of classifications was lower than in the case of simulation. We considered that recording errors were responsible for the low number of classifications and assessed the effect of recording errors on accuracy by simulation. When the recording error was lower than 10%, the loss in classification accuracy was found to be only ~3%. Therefore, we are convinced that MO-D 2 NN has high physical implementation potential.

Full Text