東華大學圖書館 |

Spintronics Logic and Memory Applications.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Spintronics Logic and Memory Applications./
作者:	Rangarajan, Nikhil .
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2020,
面頁冊數:	175 p.
附註:	Source: Dissertations Abstracts International, Volume: 81-10, Section: B.
Contained By:	Dissertations Abstracts International81-10B.
標題:	Electrical engineering. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=27666300
ISBN:	9781658450331

Spintronics Logic and Memory Applications.
Rangarajan, Nikhil .

Spintronics Logic and Memory Applications. - Ann Arbor : ProQuest Dissertations & Theses, 2020 - 175 p.

Source: Dissertations Abstracts International, Volume: 81-10, Section: B.

Thesis (Ph.D.)--New York University Tandon School of Engineering, 2020.

This item must not be sold to any third party vendors.

This thesis introduces novel state-of-the-art spin-based devices, highlights theirconstruction and working, and showcases their niche applications in implementing logic and memory. These emergent devices possess certain peculiar characteristics not afforded by CMOS, which makes them uniquely suitable for alternate logic and memory architectures. In this thesis, we leverage the unique attributes of spin devices to achieve the following tenets of logic and memory: (i) energy-efficiency, (ii) small footprint, (iii) versatility and modularity, and finally (iv) secure operation. The organization of this thesis is as follows. The first chapter establishes the fundamentals of spintronics and micromagnetics theory, which will be utilized in the later chapters. It describes the basic models, definitions and phenomenon in the realm of spintronics and also provides the mathematical foundations required to physically model and simulate a spintronic system. The purpose of this chapter is to familiarize the reader with the techniques that will leveraged for implementing various spin-based applications further along in the thesis.The second chapter focuses on the design and modeling of probabilistic logic using superparamagnetic nanomagnets, for which there exists a strong interplay between deterministic dynamics and intrinsic thermal noise. The switching element in the spin domain is chosen as the giant spin-Hall effect (GSHE) device that operates based on the dipolar coupling in a two-magnet system to achieve low energy and low-power switching characteristics. The use of spin currents in the subcritical regime to operate the GSHE device yields non-deterministic switching behavior. By utilizing the inherent thermal stochasticity, nanomagnets are promising for implementing probabilistic computing platforms targeted toward error-tolerant applications from the image processing and machine learning domains.The third chapter addresses the accelerated proliferation of portable and lowpower computing devices, which has incentivized new paradigms in low-cost reconfigurable computing. Emergent spin devices are promising in this design-space owing to their area efficiency, lower power dissipation, and reconfigurability. Here, we design a polymorphic spin-based logic for power- and area-efficient applications by exploiting the GSHE in heavy metals. The GSHE device offers substantial reductions in area and power dissipation over 45-nm CMOS devices, while improving circuit modularity over CMOS FPGAs and reconfigurable computing platforms based on emergent devices. The fourth chapter presents a spin-based true random number generator (TRNG) that uses the inherent stochasticity in nanomagnets as the source of entropy. The proposed TRNG is shown to be statistically random with 99 percent confidence levels, and immune to process and temperature variability. The TRNG is benchmarked for performance (in terms of area, throughput, and power) against existing state-of-the-art TRNGs. Overall, the proposed spin-based TRNG circuit shows significant robustness, reliability, and fidelity and, therefore, has a potential for on-chip implementation. The fifth chapter tackles the challenge of protecting intellectual property (IP) in electronic circuits by leveraging the innate polymorphism of the GSHE device, to simultaneously enable locking and camouflaging within a single instance. Using the GSHE device, we propose a powerful primitive that enables cloaking all the 16 Boolean functions possible for two inputs. We conduct a comprehensive study using state-of-the-art Boolean satisfiability (SAT) attacks to demonstrate the superior resilience of the proposed primitive in comparison to several others in the literature.The final chapter focuses on a Secure Magnetoelectric Antiferromagnet-based Tamper-Proof (SMART) memory, which leverages unique properties of antiferromagnetic materials and offers dense, on-chip non-volatile storage. SMART memory is not only resilient against data confidentiality attacks seeking to leak sensitive information but also protects data integrity and prevents Denial of Service (DoS) attacks on the memory. Further, the ultra-low power magnetoelectric switching coupled with the terahertz regime antiferromagnetic dynamics result in competitive energy-per-bit and latency numbers for the SMART memory as compared to prior non-volatile memories.

ISBN: 9781658450331Subjects--Topical Terms:

649834
Electrical engineering.
Subjects--Index Terms:

Antiferromagnetic memory

Spintronics Logic and Memory Applications.
LDR:05608nmm a2200385 4500 001 2266090
005 20200608114756.5
008 220629s2020 ||||||||||||||||| ||eng d
020 $a 9781658450331
035 $a (MiAaPQ)AAI27666300
035 $a AAI27666300
040 $a MiAaPQ $c MiAaPQ
100 1 $a Rangarajan, Nikhil . $3 3543277
245 1 0 $a Spintronics Logic and Memory Applications.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2020
300 $a 175 p.
500 $a Source: Dissertations Abstracts International, Volume: 81-10, Section: B.
500 $a Advisor: Rakheja, Shaloo.
502 $a Thesis (Ph.D.)--New York University Tandon School of Engineering, 2020.
506 $a This item must not be sold to any third party vendors.
520 $a This thesis introduces novel state-of-the-art spin-based devices, highlights theirconstruction and working, and showcases their niche applications in implementing logic and memory. These emergent devices possess certain peculiar characteristics not afforded by CMOS, which makes them uniquely suitable for alternate logic and memory architectures. In this thesis, we leverage the unique attributes of spin devices to achieve the following tenets of logic and memory: (i) energy-efficiency, (ii) small footprint, (iii) versatility and modularity, and finally (iv) secure operation. The organization of this thesis is as follows. The first chapter establishes the fundamentals of spintronics and micromagnetics theory, which will be utilized in the later chapters. It describes the basic models, definitions and phenomenon in the realm of spintronics and also provides the mathematical foundations required to physically model and simulate a spintronic system. The purpose of this chapter is to familiarize the reader with the techniques that will leveraged for implementing various spin-based applications further along in the thesis.The second chapter focuses on the design and modeling of probabilistic logic using superparamagnetic nanomagnets, for which there exists a strong interplay between deterministic dynamics and intrinsic thermal noise. The switching element in the spin domain is chosen as the giant spin-Hall effect (GSHE) device that operates based on the dipolar coupling in a two-magnet system to achieve low energy and low-power switching characteristics. The use of spin currents in the subcritical regime to operate the GSHE device yields non-deterministic switching behavior. By utilizing the inherent thermal stochasticity, nanomagnets are promising for implementing probabilistic computing platforms targeted toward error-tolerant applications from the image processing and machine learning domains.The third chapter addresses the accelerated proliferation of portable and lowpower computing devices, which has incentivized new paradigms in low-cost reconfigurable computing. Emergent spin devices are promising in this design-space owing to their area efficiency, lower power dissipation, and reconfigurability. Here, we design a polymorphic spin-based logic for power- and area-efficient applications by exploiting the GSHE in heavy metals. The GSHE device offers substantial reductions in area and power dissipation over 45-nm CMOS devices, while improving circuit modularity over CMOS FPGAs and reconfigurable computing platforms based on emergent devices. The fourth chapter presents a spin-based true random number generator (TRNG) that uses the inherent stochasticity in nanomagnets as the source of entropy. The proposed TRNG is shown to be statistically random with 99 percent confidence levels, and immune to process and temperature variability. The TRNG is benchmarked for performance (in terms of area, throughput, and power) against existing state-of-the-art TRNGs. Overall, the proposed spin-based TRNG circuit shows significant robustness, reliability, and fidelity and, therefore, has a potential for on-chip implementation. The fifth chapter tackles the challenge of protecting intellectual property (IP) in electronic circuits by leveraging the innate polymorphism of the GSHE device, to simultaneously enable locking and camouflaging within a single instance. Using the GSHE device, we propose a powerful primitive that enables cloaking all the 16 Boolean functions possible for two inputs. We conduct a comprehensive study using state-of-the-art Boolean satisfiability (SAT) attacks to demonstrate the superior resilience of the proposed primitive in comparison to several others in the literature.The final chapter focuses on a Secure Magnetoelectric Antiferromagnet-based Tamper-Proof (SMART) memory, which leverages unique properties of antiferromagnetic materials and offers dense, on-chip non-volatile storage. SMART memory is not only resilient against data confidentiality attacks seeking to leak sensitive information but also protects data integrity and prevents Denial of Service (DoS) attacks on the memory. Further, the ultra-low power magnetoelectric switching coupled with the terahertz regime antiferromagnetic dynamics result in competitive energy-per-bit and latency numbers for the SMART memory as compared to prior non-volatile memories.
590 $a School code: 1988.
650 4 $a Electrical engineering. $3 649834
650 4 $a Nanotechnology. $3 526235
650 4 $a Applied physics. $3 3343996
653 $a Antiferromagnetic memory
653 $a IC camouflaging
653 $a Probabilistic logic
653 $a Reconfigurable logic
653 $a Spintronics
653 $a True random number generator
690 $a 0544
690 $a 0652
690 $a 0215
710 2 $a New York University Tandon School of Engineering. $b Electrical and Computer Engineering. $3 3350298
773 0 $t Dissertations Abstracts International $g 81-10B.
790 $a 1988
791 $a Ph.D.
792 $a 2020
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=27666300