Inhomogeneous Metasurface–CNN System for Filter-Free Single-Pixel Wavelength Recognition

High-performance visible-light detection is vital for imaging and portable sensing, but most photodetectors are broadband intensity devices without intrinsic spectral selectivity and therefore rely on filter arrays or dispersive optics. Broadband visible photodetection with filter-free wavelength recognition is enabled by an inhomogeneous metasurface integrated with a convolutional Neural Network (CNN). Inhomogeneous metasurface creates geometry-dependent Localized Surface Plasmon Resonance (LSPR) hot spots that imprint wavelength-dependent signatures, while CNN is employed to recognize the signatures. The device shows broadband response in visible region with responsivity up to 1.45×10³ V W⁻¹, noise-equivalent power (NEP) down to 8.57 × 10 ^–16 W Hz ^1/2 , specific detectivity up to 5.71×10 ¹⁴ Jones. A lightweight 1D-CNN trained on a wavelength-distance dataset achieves 98.91% accuracy in 10-fold cross-validation and is validated by raster-scanning on a laptop display and traffic light recognition. The scalable Ag inhomogeneous metasurface/poly(3,4-ethylenedioxythiophene) polystyrene sulfonate (PEDOT:PSS) process and detector–CNN integration offer a practical route to low-cost, compact, filter-free colorful imaging and spectral identification.