東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

On-chip Photonic Systems for Machine...

Chen, Alexander.

Linked to FindBook

Google Book

Amazon

博客來

On-chip Photonic Systems for Machine Learning Applications.

Record Type:	Electronic resources : Monograph/item
Title/Author:	On-chip Photonic Systems for Machine Learning Applications./
Author:	Chen, Alexander.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2024,
Description:	118 p.
Notes:	Source: Dissertations Abstracts International, Volume: 85-12, Section: B.
Contained By:	Dissertations Abstracts International85-12B.
Subject:	Optics. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31146047
ISBN:	9798383058848

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

On-chip Photonic Systems for Machine Learning Applications. - Ann Arbor : ProQuest Dissertations & Theses, 2024 - 118 p.

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

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

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.

ISBN: 9798383058848Subjects--Topical Terms:

517925
Optics.
Subjects--Index Terms:

Integrated photonics

On-chip Photonic Systems for Machine Learning Applications.
LDR:01991nmm a2200397 4500 001 2401920
005 20241022111607.5
006 m o d
007 cr#unu||||||||
008 251215s2024 ||||||||||||||||| ||eng d
020 $a 9798383058848
035 $a (MiAaPQ)AAI31146047
035 $a AAI31146047
035 $a 2401920
040 $a MiAaPQ $c MiAaPQ
100 1 $a Chen, Alexander. $3 3772138
245 1 0 $a On-chip Photonic Systems for Machine Learning Applications.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2024
300 $a 118 p.
500 $a Source: Dissertations Abstracts International, Volume: 85-12, Section: B.
500 $a Advisor: Huang, Zhaoran Rena.
502 $a Thesis (Ph.D.)--Rensselaer Polytechnic Institute, 2024.
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.
590 $a School code: 0185.
650 4 $a Optics. $3 517925
650 4 $a Applied physics. $3 3343996
653 $a Integrated photonics
653 $a Neuromorphic computing
653 $a Photonic computing
653 $a Silicon photonics
653 $a Slow light components
690 $a 0752
690 $a 0800
690 $a 0215
710 2 $a Rensselaer Polytechnic Institute. $b Physics. $3 3168682
773 0 $t Dissertations Abstracts International $g 85-12B.
790 $a 0185
791 $a Ph.D.
792 $a 2024
793 $a English
856 4 0 $u https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31146047