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
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
