Abstract
A Bayesian-based method is developed to infer the distribution of systematic geometric variations in silicon photonics. Width, thickness, and partial etch depth variation distributional maps are reported for silicon nitride ring resonator characterization measurements.
Cite
CITATION STYLE
APA
Zhang, Z., El-Henawy, S. I., Ríos, C., & Boning, D. S. (2022). Inference of Process Variations in Silicon Photonics from Characterization Measurements. In 2022 Conference on Lasers and Electro-Optics, CLEO 2022 - Proceedings. Institute of Electrical and Electronics Engineers Inc. https://doi.org/10.1364/oe.489164
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