國立虎尾科技大學 |

On-chip Photonic Systems for Machine Learning Applications.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	On-chip Photonic Systems for Machine Learning Applications./
作者:	Chen, Alexander.
面頁冊數:	1 online resource (118 pages)
附註:	Source: Dissertations Abstracts International, Volume: 85-12, Section: B.
Contained By:	Dissertations Abstracts International85-12B.
標題:	Optics. -
電子資源:	click for full text (PQDT)
ISBN:	9798383058848

On-chip Photonic Systems for Machine Learning Applications.
Chen, Alexander.

On-chip Photonic Systems for Machine Learning Applications. - 1 online resource (118 pages)

Source: Dissertations Abstracts International, Volume: 85-12, Section: B.

Thesis (Ph.D.)--Rensselaer Polytechnic Institute, 2024.

Includes bibliographical references

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.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798383058848Subjects--Topical Terms:

595336
Optics.
Subjects--Index Terms:

Integrated photonicsIndex Terms--Genre/Form:

554714
Electronic books.

On-chip Photonic Systems for Machine Learning Applications.
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504 $a Includes bibliographical references
520 $a 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.
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653 $a Neuromorphic computing
653 $a Photonic computing
653 $a Silicon photonics
653 $a Slow light components
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