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Diffractive Deep Neural Networks at Visible Wavelengths
Engineering ( IF 12.8 ) Pub Date : 2021-02-13 , DOI: 10.1016/j.eng.2020.07.032
Hang Chen , Jianan Feng , Minwei Jiang , Yiqun Wang , Jie Lin , Jiubin Tan , Peng Jin

Optical deep learning based on diffractive optical elements offers unique advantages for parallel processing, computational speed, and power efficiency. One landmark method is the diffractive deep neural network (D2NN) based on three-dimensional printing technology operated in the terahertz spectral range. Since the terahertz bandwidth involves limited interparticle coupling and material losses, this paper extends D2NN to visible wavelengths. A general theory including a revised formula is proposed to solve any contradictions between wavelength, neuron size, and fabrication limitations. A novel visible light D2NN classifier is used to recognize unchanged targets (handwritten digits ranging from 0 to 9) and targets that have been changed (i.e., targets that have been covered or altered) at a visible wavelength of 632.8 nm. The obtained experimental classification accuracy (84%) and numerical classification accuracy (91.57%) quantify the match between the theoretical design and fabricated system performance. The presented framework can be used to apply a D2NN to various practical applications and design other new applications.



中文翻译:

可见波长下的衍射深度神经网络

基于衍射光学元件的光学深度学习在并行处理、计算速度和功率效率方面具有独特的优势。一种具有里程碑意义的方法是基于在太赫兹光谱范围内运行的三维打印技术的衍射深度神经网络 (D 2 NN)。由于太赫兹带宽涉及有限的粒子间耦合和材料损耗,本文将 D 2 NN扩展到可见光波长。提出了一种包括修正公式的一般理论来解决波长、神经元大小和制造限制之间的任何矛盾。一种新型可见光 D 2NN分类器用于在632.8 nm的可见波长下识别未改变的目标(手写数字范围从0到9)和已经改变的目标(即被覆盖或改变的目标)。获得的实验分类精度 (84%) 和数值分类精度 (91.57%) 量化了理论设计与制造系统性能之间的匹配。所提出的框架可用于将 D 2 NN 应用于各种实际应用并设计其他新应用。

更新日期:2021-02-13
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