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On chip optical neural networks based on MMI microring resonators for image classification
 T.T. Bui 1, D.T. Le 2, T.H.L. Nguyen 3, T.T. Le 2
 1 FPT University Hoa Lac High Tech Park, 100000, Hanoi, Viet Nam;
     2 International School (VNU-IS), Vietnam National University (VNU)-Hanoi,
     100000, Hanoi, Viet Nam, Xuan Thuy St. 144;
     3 Hanoi University of Natural Resources and Environment,
     100000, Hanoi, Viet Nam, Phu Dien St. 41A
 
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 PDF, 2823 kB
DOI: 10.18287/2412-6179-CO-1211
Страницы: 588-595.
Язык статьи: English.
 
Аннотация:
We propose a new on-chip optical neural network (OONN) based on  multimode interference-microring resonators (MMI-RRs). The suggested structure  eliminates the need for wavelength division multiplexers (WDM) to create an  optical neuron on a single chip. New microring resonator structure based on 4×4  MMI coupler with a size of 24µm × 2900 µm  is used for the basic elements of the computation matrix, as a result a higher  bandwidth and free spectral range (FSR) can be achieved. The Si3N4 platform  along with the graphene sheet is designed to modulate the signals and weights  of the neural networks at a very high speed. The Si3N4 can provide wide range  of operating wavelengths and can work directly with the wavelengths of color images.  The structure's benefits include rapid computing speed, little loss, and the  ability to handle both positive and negative values. The OONN has been applied  to the MNIST dataset with a speed faster than 2.8 to 14x times compared with  the conventional GPU methods.
Ключевые слова:
all-optical dot product, image processing, multimode interference coupler, optical convolutional neural networks, optical signal processing, microring resonators, silicon photonics.
Благодарности
This research is funded by Vietnam National Founda-tion for Science and Technology Development (NA-FOSTED) under grant number 103.03-2018.354.
Citation:
Bui TT, Le DT, Nguyen THL and Le TT. On chip optical neural networks based on MMI microring resonators for image classification. Computer Optics 2023; 47(4): 588-595. DOI: 10.18287/2412-6179-CO-1211.
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