Diffractive Deep Neural Networks at Visible Wavelengths

• 作者: Hang Chen Jianan Feng Minwei Jiang Yiqun Wang Jie Lin Jiubin Tan Peng Jin
• 来源期刊: 工程（英文）
• 年，卷（期）: 2021,（10）
• 所属期刊栏目: Optical Neural Networks-Article
• 页码范围: 1483-1491
• 页数: 9页
• 语种: 英文

摘要

Optical deep learning based on diffractive optical elements offers unique advantages for parallel process-ing,computational speed,and power efficiency.One landmark method is the diffractive deep neural net-work(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 tar-gets 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 pre-sented framework can be used to apply a D2NN to various practical applications and design other new applications.