Extraction of Silicon Photonic Wafer-Scale Process Variability Using ML-Enhanced Algorithm
An experimental investigation has been conducted to extract the silicon photonic wafer-scale fabrication process variability using a machine-learning enhanced algorithm to predict sub-nanometer scale width, height, and effective index variations <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(\Delta n\sim 10^{-5})$</tex> for a single-mode waveguide.
Authors
- Shruti Pandey (ORCID: https://orcid.org/0000-0002-5269-1582)
- Anjan Chakravorty (ORCID: https://orcid.org/0000-0002-5253-8975)
- Arnab Goswami (ORCID: https://orcid.org/0000-0002-2104-0875)
- Deleep R. Nair (ORCID: https://orcid.org/0000-0002-8563-9430)
- Anjana James
- Gan Yih Loong
- Bijoy Krishna Das (ORCID: https://orcid.org/0000-0002-4647-9980)
- Ashitosh Velamuri (ORCID: https://orcid.org/0009-0002-4888-3044)
- Tarun Arumugham
- Ng Chew Yan
Institutions
- Indian Institute of Technology Madras (IN)
- Jabatan Perkhidmatan Awam Malaysia (MY)
Publication Details
- Published
- 2024-08-04
- DOI
- https://doi.org/10.1109/cleo-pr60912.2024.10676454
- Primary Topic
- Image Processing Techniques and Applications
- Type
- article
- Field-Weighted Citation Impact
- 0.00