On-chip photonic systems for machine learning applications
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Authors
Chen, Alexander
Issue Date
2024-04
Type
Electronic thesis
Thesis
Thesis
Language
en_US
Keywords
Physics
Alternative Title
Abstract
This dissertation presents a comprehensive study on the integration of photonic systems within silicon chips to enhance machine learning applications. It explores the potential of on-chip photonic components, such as low-loss silicon nitride delay lines and slow light thermal phase shifters, in overcoming the limitations faced by traditional electronic computing systems. Through detailed design, fabrication, and characterization, this work demonstrates how these photonic components can be utilized in neural network models, offering scalable and energy-efficient solutions for real-time data processing and advanced cognitive tasks. This research marks a significant step toward the realization of next-generation machine learning hardware, leveraging the unique advantages of photonic computing.
Description
April2024
School of Science
School of Science
Full Citation
Publisher
Rensselaer Polytechnic Institute, Troy, NY