Diffractive Deep Neural Networks at Visible Wavelengths  

作　　者：Hang Chen Jianan Feng Minwei Jiang Yiqun Wang Jie Lin Jiubin Tan Peng Jin 

机构地区：[1]Center of Ultra-precision Optoelectronic Instrument,Harbin Institute of Technology,Harbin 150001,China [2]Nanofabrication Facility,Suzhou Institute of Nano-Tech and Nano-Bionics,Chinese Academy of Sciences,Suzhou 215123,China [3]Key Laboratory of Micro-Systems and Micro-Structures Manufacturing,Ministry of Education,Harbin Institute of Technology,Harbin 150001,China

出　　处：《Engineering》2021年第10期1483-1491,共9页工程（英文）

基　　金：This research was supported in part by National Natural Science Foundation of China(61675056 and 61875048).

摘　　要：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(D^(2) NN)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 D^(2) NN 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 D^(2) NN 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 D^(2) NN to various practical applications and design other new applications.

关 键 词：Optical computation Optical neural networks Deep learning Optical machine learning Diffractive deep neural networks 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程] TP391.41[自动化与计算机技术—控制科学与工程]