Diffractive Deep Neural Networks at Visible Wavelengths. (October 2021)

• Record Type: Journal Article
• Authors: Chen, Hang; Feng, Jianan; Jiang, Minwei; Wang, Yiqun; Lin, Jie; Tan, Jiubin; Jin, Peng
• Is Part Of: Engineering. Volume 7:Number 10(2021)
• Page Start: 1483
• Page End: 1491
• Publication Date: 2021-10
• Subjects: Optical computation -- Optical neural networks -- Deep learning -- Optical machine learning -- Diffractive deep neural networks; Engineering -- Periodicals; Engineering -- China -- Periodicals; 620.005
• DOI: 10.1016/j.eng.2020.07.032
• Languages: English
• ISSNs: 2095-8099

Abstract

Graphical abstract: Highlights: A general method that extends diffractive deep neural networks from terahertz spectrum to visible wavelengths is proposed. An improved formula is proposed to solve contradictions between wavelength, neuron size and fabrication limitation. The diffractive deep neural networks at visible wavelengths can implement covered and altered target classification. Abstract: 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.