1T'-MoTe₂ as an integrated saturable absorber for photonic machine learning

2025/07/22 by Maria Carolina Volpato, Henrique G. Rosa, Volpato, Maria Carolina +7 · 1 citation
Computer Science · Engineering · #Applied Physics (physics.app-ph) #FOS: Physical sciences #Neural Networks and Reservoir Computing #Optical Network Technologies #Optics (physics.optics) #Photonic and Optical Devices

paper · pdf · doi:10.48550/arxiv.2507.16140

openalex publication_date 2025/07/22 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

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, 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'-MoTe₂ monolayer and the silicon nitride waveguide, with saturation achievable at input powers as low as a few uW. These results suggest that 1T'-MoTe₂ is a promising candidate for implementing nonlinear functions in integrated photonic neural networks.

Citations

• Self-calibrated Microring Weight Function for Neuromorphic Optical Computing
• All-Photonic Artificial Neural Network Processor Via Non-linear Optics
• Programmable photonic circuits
• Deep learning with coherent nanophotonic circuits
• Deterministic transfer of two-dimensional materials by all-dry\n viscoelastic stamping
• Atomic layer graphene as saturable absorber for ultrafast pulsed lasers

Cited by

• Hybrid integration of two-dimensional dichalcogenides for low power saturable absorption in photonic integrated circuits

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