A methodology combining automated mode identification with multi-mode ringdown measurements quantifies intrinsic quality factor Q_intr in silicon nitride membranes, revealing thickness-dependent losses not captured by established models and explained via a phenomenological thickness-dependent loss m
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Surface modification experiments on SiNx resonators indicate that hydroxyl groups contribute to mechanical dissipation and that silanization can reduce it.
Neural networks and random forests predict surface roughness from laser parameters and material data with high accuracy, speeding up optimization and reducing experimental effort.
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Determination of the intrinsic mechanical quality factor in high-stress silicon nitride resonators
A methodology combining automated mode identification with multi-mode ringdown measurements quantifies intrinsic quality factor Q_intr in silicon nitride membranes, revealing thickness-dependent losses not captured by established models and explained via a phenomenological thickness-dependent loss m
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HF Etching and Silanization: Evidence for the Role of Surface Hydroxyl Groups in Silicon Nitride Resonator Loss
Surface modification experiments on SiNx resonators indicate that hydroxyl groups contribute to mechanical dissipation and that silanization can reduce it.
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Enhancing Laser Surface Texturing through Advanced Machine Learning Techniques
Neural networks and random forests predict surface roughness from laser parameters and material data with high accuracy, speeding up optimization and reducing experimental effort.