1T′-MoTe2 as an integrated saturable absorber for photonic machine learning
Maria Carolina Volpato, Henrique G. Rosa, Tom Reep, Pierre-Louis de Assis, Newton C. Frateschi · Applied Physics Letters · 2025
We investigate the saturable absorption behavior of a 1T′-MoTe2 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, 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′-MoTe2 monolayer and the silicon nitride waveguide, with saturation achievable at input powers as low as a few microwatts. These results suggest that 1T′-MoTe2 is a promising candidate for implementing nonlinear functions in integrated photonic neural networks.