All-optical diffractive neural networks process broadband light
The Californer/10055183

LOS ANGELES - Californer -- Diffractive deep neural network is an optical machine learning framework that blends deep learning with optical diffraction and light-matter interaction to engineer diffractive surfaces that collectively perform optical computation at the speed of light. A diffractive neural network is first designed in a computer using deep learning techniques, followed by the physical fabrication of the designed layers of the neural network using e.g., 3D printing or lithography.

Developed by researchers at UCLA [1-3], diffractive optical networks provide a low power, low latency and highly-scalable machine learning platform. In these earlier demonstrations, diffractive network models were developed to process information through a single wavelength. Addressing this limitation, UCLA researchers have designed diffractive networks that can process information using a continuum of wavelengths, expanding this all-optical computation framework into broadband optical signals [4]. Published in Light: Science & Applications, UCLA researchers demonstrated the success of this new framework by creating a series of optical components that filter broadband input light into desired sub-bands. These deep learning-based diffractive systems also control the precise location of each filtered band of radiation at the output plane, demonstrating spatially-controlled wavelength de-multiplexing in terahertz (THz) part of the electromagnetic spectrum. After their design in a computer, these broadband diffractive networks were fabricated with a 3D-printer and successfully tested using a pulsed THz source emitting a continuum of wavelengths between 60 and 3,000 micrometers.

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This research was led by Dr. Aydogan Ozcan, UCLA Chancellor's Professor of electrical and computer engineering (ECE). The other authors of this work are graduate students Yi Luo, Deniz Mengu, Muhammed Veli, post-doctoral researcher Dr. Nezih T. Yardimci, Adjunct Professor Dr. Yair Rivenson, as well as Professor Mona Jarrahi, all with the ECE department at UCLA.

This new method is also broadly applicable to different parts of the electromagnetic spectrum, including the visible band, and thus, represents a critical milestone for diffractive optical networks toward their widespread utilization in modern day optical components and machine learning systems, covering a wide range of applications in for example robotics, autonomous vehicles and surveillance.

Link to the paper: https://www.nature.com/articles/s41377-019-0223-1

References:
1. Lin X, et al. All-optical machine learning using diffractive deep neural networks. Science 2018; 361: 1004.

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2. Li J, et al. Class-specific differential detection in diffractive optical neural networks improves inference accuracy. Adv Photon 2019; 1: 1.
3. Mengu D, et al. Analysis of Diffractive Optical Neural Networks and Their Integration With Electronic Neural Networks. IEEE Journal of Selected Topics in Quantum Electronics 2020; 26: 1–14.
4. Y. Luo, et al. "Design of task-specific optical systems using broadband diffractive neural networks," Light: Science & Applications, DOI: 10.1038/s41377-019-0223-1 (2019)