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Emulating Advanced Neural Functions With Spin Textures for Neuromorphic Computing /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Emulating Advanced Neural Functions With Spin Textures for Neuromorphic Computing /Can Cui.
作者:	Cui, Can,
面頁冊數:	1 electronic resource (122 pages)
附註:	Source: Dissertations Abstracts International, Volume: 86-04, Section: B.
附註:	Advisors: Incorvia, Jean Anne Committee members: Banerjee, Sanjay; Akinwande, Deji; Shankar, Shyam; Li, Xiaoqin.
Contained By:	Dissertations Abstracts International86-04B.
標題:	Computer engineering.
電子資源:	http://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:

212944
Computer engineering.
Subjects--Index Terms:

Skyrmion dynamics

Emulating Advanced Neural Functions With Spin Textures for Neuromorphic Computing /
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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.
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