Fig. 4: Two multi-channel holographic devices designed by parameters-to-points prediction neural network.
From: Deep learning enabled topological design of exceptional points for multi-optical-parameter control
The first black box is the Wavelength Division Multiplex (WDM) metasurface: (a) Phase and amplitude profiles around exceptional points (EPs) in the three-dimensional (L1, L2 and L3) parameter space at λ = 532 nm and 633 nm. Structures marked 1 to 4 constitute a complete base covering 2π (2-phase gradient). b The WDM metasurfaces at 532 nm and 633 nm are realized by a 2-bit coding method. The second black box is the amplitude-phase multiplexing metasurface at λ = 633 nm: (c) The phase and amplitude data of S21 (pink point) fill the entire polar space, allowing us to regulate the two degrees of freedom independently and completely. 4-level amplitude and 4-levels phase data of S21 response are selected (red star) while satisfied S12 response remain basically constant (blue point). Each location has multiple candidates. 16 meta-atom designs are selected so that the amplitude and phase are decoupled in S21 (d) and the meta-atom library is created (e). f The phase in S21 and S12 decoupled without crosstalk through the combination of the Pancharatnam-Berry (PB) phase and exceptional topological (ET) phase.
