1T'-MoTe$_2$ as an integrated saturable absorber for photonic machine learning

Abstract

We investigate the saturable absorption behavior of a 1T'-MoTe$_2$ monolayer integrated with a silicon nitride waveguide for applications in photonic neural networks. Using experimental transmission measurements and theoretical modeling, we characterize the nonlinear response of the material. Our model, incorporating quasi-Fermi level separation and carrier dynamics, successfully explains these behaviors and predicts the material's absorption dependence on the carrier density. Furthermore, we demonstrate a coupling efficiency of up to 20% between the 1T'-MoTe$_2$ monolayer and the silicon nitride waveguide, with saturation achievable at input powers as low as a few uW. These results suggest that 1T'-MoTe$_2$ is a promising candidate for implementing nonlinear functions in integrated photonic neural networks.