Отрывок: a) b) Fig. 3. (a) Waveguide graphene structure, (b) mode profile a) b) c) Fig. 4. Effective refractive index of the GSW waveguide The normalized powers at the through and drop ports of the MMI-based resonator are shown in Fig. 5a. The difference power between the two ports is in the range of (–1, +1) for negative values of the kernel filters. In this simulation, the chemical potential at the graphene is 0.6eV. By cont...
Название : | On chip optical neural networks based on MMI microring resonators for image classification |
Авторы/Редакторы : | Bui, T.T. Le, D.T. Nguyen, T.H.L. Le, T.T. |
Ключевые слова : | all-optical dot product image processing multimode interference coupler optical convolutional neural networks optical signal processing microring resonators silicon photonics |
Дата публикации : | Авг-2023 |
Издательство : | Самарский национальный исследовательский университет |
Библиографическое описание : | 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. |
Серия/номер : | 47;4 |
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. |
URI (Унифицированный идентификатор ресурса) : | https://dx.doi.org/10.18287/2412-6179-CO-1211 http://repo.ssau.ru/handle/Zhurnal-Komputernaya-optika/On-chip-optical-neural-networks-based-on-MMI-microring-resonators-for-image-classification-107766 |
Другие идентификаторы : | Dspace\SGAU\20231228\107766 |
Располагается в коллекциях: | Журнал "Компьютерная оптика" |
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