September 24, 2021
by UCLA Engineering Institute for Technology Advancement
Different forms of linear transformations, such as the Fourier transform, are widely employed in processing of information in various applications. These transformations are generally implemented in the digital domain using electronic processors, and their computation speed is limited with the capacity of the electronic chip being used, which sets a bottleneck as the data and image size get large. A remedy of this problem might be to replace digital processors with optical counterparts and use light to process information.
In a new paper published in Light: Science & Applications, a team of optical engineers, led by Professor Aydogan Ozcan from the Electrical and Computer Engineering Department at the University of California, Los Angeles (UCLA), U.S., and co-workers have developed a deep learning-based design method for all-optical computation of an arbitrary linear transform. This all-optical processor uses spatially-engineered diffractive surfaces in manipulating optical waves and computes any desired linear transform as the light passes through a series of diffractive surfaces. This way, the computation of the desired linear transform is completed at the speed of light propagation, with the transmission of the input light through these diffractive surfaces. In addition to its computational speed, these all-optical processors also do not consume any power to compute, except for the illumination light, making it a passive and high-throughput computing system.