Inverse design of ultracompact multi-focal optical devices by diffractive neural networks

Abstract

We propose an efficient inverse design approach for multifunctional optical elements based on adaptive deep diffractive neural networks (a-D${}^2$NNs). Specifically, we introduce a-D${}^2$NNs and design two-layer diffractive devices that can selectively focus incident radiation over two well-separated spectral bands at desired distances. We investigate focusing efficiencies at two wavelengths and achieve targeted spectral lineshapes and spatial point-spread functions (PSFs) with optimal focusing efficiency. In particular, we demonstrate control of the spectral bandwidths at separate focal positions beyond the theoretical limit of single-lens devices with the same aperture size. Finally, we demonstrate devices that produce super-oscillatory focal spots at desired wavelengths. The proposed method is compatible with current diffractive optics and doublet metasurface technology for ultracompact multispectral imaging and lensless microscopy applications.

References in corpus (1)

• Understanding and mitigating gradient pathologies in physics-informed neural networks

Cited by in corpus (1)

• Design of ultracompact broadband focusing spectrometers based on deep diffractive neural networks