Miniaturized Diffraction Grating Design and Processing for Deep Neural Network

Researchers of UCLA reported the fully connected optical neural network, the high computational rate, and low power consumption was realized, while the diffraction grating system based on the Terahertz source is expensive and bulky. In this letter, a long-wave infrared source with a wavelength of 10.6 um is used to establish an optical neural network transfer model using the Sommerfeld diffraction theory. Diffraction grating design, processing, and error analysis applied to deep neural networks are carried out. The MNIST handwritten database is used as the data set to train and optimize the phase parameters by forward propagation and backpropagation. The neuron size is 5um; the number of neurons is 200*200; the entire grating area is 1mm. Compared with the existing light diffraction neural network, the feature size of the deep learning neural network is reduced by 80 times. The Ge (Germanium)-based diffraction grating of 5 layers of neurons with four relative step heights is engraved by semiconductor standard processing technology. The surface-infrared high-efficiency anti-reflection film increased the grating transmission efficiency to over 90%.

[1]  J. Goodman.  Introduction to Fourier optics , 1969 .

[2]  Martin Wegener,et al.  Tailored 3D Mechanical Metamaterials Made by Dip‐in Direct‐Laser‐Writing Optical Lithography , 2012, Advanced materials.

[3]  Jonghyun Choi,et al.  Training with the Invisibles: Obfuscating Images to Share Safely for Learning Visual Recognition Models , 2019, ArXiv.

[4]  Jianfeng Gao,et al.  Deep Learning for Web Search and Natural Language Processing , 2015 .

[5]  Yi Luo,et al.  All-optical machine learning using diffractive deep neural networks , 2018, Science.

[6]  Yoshua Bengio,et al.  Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation , 2014, EMNLP.

[7]  Yi Luo,et al.  Analysis of Diffractive Optical Neural Networks and Their Integration With Electronic Neural Networks , 2018, IEEE Journal of Selected Topics in Quantum Electronics.

[8]  Catherine S. Jarnevich,et al.  Modelling invasion for a habitat generalist and a specialist plant species , 2008 .

[9]  Yi Luo,et al.  Class-specific differential detection in diffractive optical neural networks improves inference accuracy , 2019, Advanced Photonics.

[10]  Ronald D. Schaeffer,et al.  Fundamentals of Laser Micromachining , 2012 .

[11]  W L Wolfe,et al.  Refractive indexes and temperature coefficients of germanium and silicon. , 1976, Applied optics.

[12]  Hongbin Wang,et al.  Comment on All-optical machine learning using diffractive deep neural networks , 2018, ArXiv.

[13]  Yi Luo,et al.  Response to Comment on "All-optical machine learning using diffractive deep neural networks" , 2018, ArXiv.