Инновационная архитектура D2NN представляет собой полностью оптическую нейросеть, созданную с помощью 3D-печати. Этот ИИ обучается распознавать рукописные цифры, используя пассивные дифракционные слои, что открывает путь к сверхбыстрой обработке визуальных данных практически без затрат электроэнергии.
We introduce an all-optical Diffractive Deep Neural Network (D2NN) architecture that can learn to implement various functions after deep learning-based design of passive diffractive layers that work collectively. We experimentally demonstrated the success of this framework by creating 3D-printed D2NNs that learned to implement handwritten digit classification and the function of an imaging lens at terahertz spectrum. With the existing plethora of 3D-printing and other lithographic fabrication methods as well as spatial-light-modulators, this all-optical deep learning framework can perform, at the speed of light, various complex functions that computer-based neural networks can implement, and will find applications in all-optical image analysis, feature detection and object classification, also enabling new camera designs and optical components that can learn to perform unique tasks using D2NNs.