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

Emulating Advanced Neural Functions ...

Cui, Can,

Linked to FindBook

Google Book

Amazon

博客來

Emulating Advanced Neural Functions With Spin Textures for Neuromorphic Computing /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Emulating Advanced Neural Functions With Spin Textures for Neuromorphic Computing // Can Cui.
Author:	Cui, Can,
Description:	1 electronic resource (122 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 86-04, Section: B.
Contained By:	Dissertations Abstracts International86-04B.
Subject:	Computer engineering. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31690549
ISBN:	9798384442394

Emulating Advanced Neural Functions With Spin Textures for Neuromorphic Computing /
Cui, Can,

Emulating Advanced Neural Functions With Spin Textures for Neuromorphic Computing /Can Cui. - 1 electronic resource (122 pages)

Source: Dissertations Abstracts International, Volume: 86-04, Section: B.

Neuromorphic computing calls for devices that exhibit intrinsic neuronal functions for compact, energy efficient hardware implementations. Spintronic devices allow for electrical or magnetic control of the spin polarization of the computing media and are gaining importance in neuromorphic computing due to benefits in endurance, energy efficiency and CMOS processing compatibility. Most importantly, their reconfigurable spin textures provide additional flexibility in mimicking the complex, dynamic behaviors of the biological neurons and synapses.This dissertation explores the mapping of neuronal functions onto spin textures within ferromagnetic thin films, particularly focusing on domain walls (DW) and skyrmions. Through a combination of device modeling and experimental studies, I aim to demonstrate that the manipulability of these spin textures holds significant promise for neuromorphic computing applications. I will introduce the fundamental concepts of magnetism and magnetic materials that lay the groundwork for understanding DW and skyrmion dynamics. The integrated DW-magnetic tunnel junction (MTJ) device is first discussed as a basis for spintronic neuromorphic applications. Device-level micromagnetic modeling reveals the mechanism behind the intrinsic lateral inhibition (LI) phenomena between DW-MTJ integrate-fire neurons, and I will show the methodology to maximize LI, which is essential for regulating neuronal activities and facilitating winner-take-all (WTA) functions. A novel adaptive neuron is designed by leveraging the rich dynamics of skyrmions to emulate advanced cognitive functions such as adaptive responses and cross-frequency coupling (CFC). Device functionality is validated using micromagnetic modeling, and their superiority in context-aware learning is demonstrated through neural network modeling.The experimental study focuses on the design and fabrication of a DW-MTJ integrate-fire neuron device which improves the DW injection and reset characteristics of previous implementations. Fabrication techniques and optimization are presented, along with experimental demonstration of device functionality. I highlight the future integration of neuron and synapse devices into a monolithic, all-spintronic neuromorphic network, paving the way for stream learning applications. Overall, this dissertation contributes to advancing the application of spintronic devices in neuromorphic computing, offering insights into the potential of spin textures for next-generation cognitive computing systems.

English

ISBN: 9798384442394Subjects--Topical Terms:

621879
Computer engineering.
Subjects--Index Terms:

Skyrmion dynamics

Emulating Advanced Neural Functions With Spin Textures for Neuromorphic Computing /
LDR:04098nmm a22004573i 4500 001 2428966
005 20250420214400.5
006 m o d
007 cr|nu||||||||
008 261011s2024 miu||||||m |||||||eng d
020 $a 9798384442394
035 $a (MiAaPQD)AAI31690549
035 $a (MiAaPQD)123vireo24677Cui
035 $a AAI31690549
040 $a MiAaPQD $b eng $c MiAaPQD $e rda
100 1 $a Cui, Can, $e author. $3 3816523
245 1 0 $a Emulating Advanced Neural Functions With Spin Textures for Neuromorphic Computing / $c Can Cui.
264 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2024
300 $a 1 electronic resource (122 pages)
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
500 $a Source: Dissertations Abstracts International, Volume: 86-04, Section: B.
500 $a Advisors: Incorvia, Jean Anne Committee members: Banerjee, Sanjay; Akinwande, Deji; Shankar, Shyam; Li, Xiaoqin.
502 $b Ph.D. $c The University of Texas at Austin $d 2024.
520 $a Neuromorphic computing calls for devices that exhibit intrinsic neuronal functions for compact, energy efficient hardware implementations. Spintronic devices allow for electrical or magnetic control of the spin polarization of the computing media and are gaining importance in neuromorphic computing due to benefits in endurance, energy efficiency and CMOS processing compatibility. Most importantly, their reconfigurable spin textures provide additional flexibility in mimicking the complex, dynamic behaviors of the biological neurons and synapses.This dissertation explores the mapping of neuronal functions onto spin textures within ferromagnetic thin films, particularly focusing on domain walls (DW) and skyrmions. Through a combination of device modeling and experimental studies, I aim to demonstrate that the manipulability of these spin textures holds significant promise for neuromorphic computing applications. I will introduce the fundamental concepts of magnetism and magnetic materials that lay the groundwork for understanding DW and skyrmion dynamics. The integrated DW-magnetic tunnel junction (MTJ) device is first discussed as a basis for spintronic neuromorphic applications. Device-level micromagnetic modeling reveals the mechanism behind the intrinsic lateral inhibition (LI) phenomena between DW-MTJ integrate-fire neurons, and I will show the methodology to maximize LI, which is essential for regulating neuronal activities and facilitating winner-take-all (WTA) functions. A novel adaptive neuron is designed by leveraging the rich dynamics of skyrmions to emulate advanced cognitive functions such as adaptive responses and cross-frequency coupling (CFC). Device functionality is validated using micromagnetic modeling, and their superiority in context-aware learning is demonstrated through neural network modeling.The experimental study focuses on the design and fabrication of a DW-MTJ integrate-fire neuron device which improves the DW injection and reset characteristics of previous implementations. Fabrication techniques and optimization are presented, along with experimental demonstration of device functionality. I highlight the future integration of neuron and synapse devices into a monolithic, all-spintronic neuromorphic network, paving the way for stream learning applications. Overall, this dissertation contributes to advancing the application of spintronic devices in neuromorphic computing, offering insights into the potential of spin textures for next-generation cognitive computing systems.
546 $a English
590 $a School code: 0227
650 4 $a Computer engineering. $3 621879
650 4 $a Electromagnetics. $3 3173223
650 4 $a Condensed matter physics. $3 3173567
650 4 $a Information technology. $3 532993
653 $a Skyrmion dynamics
653 $a Lateral inhibition
653 $a Winner-take-all functions
653 $a Spintronic devices
653 $a Spin polarization
690 $a 0489
690 $a 0464
690 $a 0611
690 $a 0607
710 2 $a The University of Texas at Austin. $b Electrical and Computer Engineering. $e degree granting institution. $3 3816524
720 1 $a Incorvia, Jean Anne $e degree supervisor.
773 0 $t Dissertations Abstracts International $g 86-04B.
790 $a 0227
791 $a Ph.D.
792 $a 2024
856 4 0 $u https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31690549