Hybrid Random Forest and MLP Surrogate Modeling for a Fully Tunable Acousto-optic Filter Based on Photonic Crystal Heterojunction
Abstract
samples. Two models, Random Forest and MLP, are trained and compared; Random Forest offers competitive accuracy and feature importance, while MLP enables rapid parameter sweeps. Results demonstrate a tuning range of 1478–1556 nm for center wavelength and 4.5–8 nm
for bandwidth. Under ±10 nm geometric tolerance, wavelength and bandwidth variations remain within 0.4 nm and 0.5 nm respectively. This
machine-learning-assisted approach preserves the device's tunability, integration, and fault tolerance while significantly accelerating performance prediction and parameter exploration, offering a practical route for data-driven design in tunable photonic devices.
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DOI: http://dx.doi.org/10.70711/aitr.v4i3.9926
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