Issue |
EPJ Web Conf.
Volume 287, 2023
EOS Annual Meeting (EOSAM 2023)
|
|
---|---|---|
Article Number | 13004 | |
Number of page(s) | 2 | |
Section | Focused Sessions (FS) 4- Machine Learning and Photonic Artificial Intelligence / Optical Neural Networks and Neuromorphic Computing | |
DOI | https://doi.org/10.1051/epjconf/202328713004 | |
Published online | 18 October 2023 |
https://doi.org/10.1051/epjconf/202328713004
Advances in machine learning for large-scale manufacturing of photonic circuits
Enablence Technologies Inc., 390 March Road, Ottawa, Canada K2K 0G7
* Corresponding author: ksenia.yadav@enablence.com
Published online: 18 October 2023
Machine learning has opened a new realm of possibilities in photonic circuit design and manufacturing. First, we describe our approach of using deep learning to optimize the multi-dimensional parameter space for hundreds of optical chips on a mask, resulting in homogeneity of performance in high volume applications. Second, we present our approach of using a support vector machine to predict the performance of optical devices by wafer probing. This approach eliminates the expensive and labour-intensive process of optical chip testing, and allows unprecedented control over the fabrication process, including in-situ monitoring of wafer fabrication and real-time process adjustments. The combination of these two approaches paves the way for accelerated adoption of photonics in high volume applications.
© The Authors, published by EDP Sciences, 2023
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