Multiplexed metasurface optical neural networks

Abstract

The present disclosure provides a multiplexed metasurface optical neural network device including metasurface layers that modify amplitude and phase of incident light to perform distinct classification tasks, each task associated with a degree of freedom including wavelength or polarization, and a detector that captures output intensity information encoding classification weights. A method includes projecting light intensity profiles representing objects onto metasurface layers using different wavelengths or polarization states, and capturing output intensity information corresponding to classification into different sets of classes. The device enables handwritten digit recognition at one wavelength or polarization state and object classification at another. The device also operates as a generative model, with metasurface layers generating diverse output images in response to random input light intensity profiles through spatial multiplexing, where random profiles are derived from a standard Gaussian distribution representing latent variables.

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