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Two Dimensional Materials Based Memr...

Xie, Jing.

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Two Dimensional Materials Based Memristors for In-Memory Computing and Neuromorphic Computing.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Two Dimensional Materials Based Memristors for In-Memory Computing and Neuromorphic Computing./
作者:	Xie, Jing.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2023,
面頁冊數:	104 p.
附註:	Source: Dissertations Abstracts International, Volume: 85-06, Section: B.
Contained By:	Dissertations Abstracts International85-06B.
標題:	Electrical engineering. -
電子資源:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30696647
ISBN:	9798381158151

Two Dimensional Materials Based Memristors for In-Memory Computing and Neuromorphic Computing.
Xie, Jing.

Two Dimensional Materials Based Memristors for In-Memory Computing and Neuromorphic Computing. - Ann Arbor : ProQuest Dissertations & Theses, 2023 - 104 p.

Source: Dissertations Abstracts International, Volume: 85-06, Section: B.

Thesis (Ph.D.)--Arizona State University, 2023.

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

Modern Complementary-Metal-Oxide-Semiconductor (CMOS) technologies are facing critical challenges: scaling channel lengths below ~10 nm is hindered by significant transport degradation as bulk semiconductors (i.e., silicon) are thinned down, energy consumption is affected by short-channel effects and off-state leakage, and conventional von Neumann computing architectures face serious bottlenecks affecting performance and efficiency (energy consumption and throughput). Neuromorphic and/or in-memory computing architectures using resistive random-access memory (RRAM) crossbar arrays are promising candidates to mitigate these bottlenecks and to circumvent CMOS scaling challenges. Recently, emerging two dimensional materials (2DMs) are investigated towards ultra-scaled CMOS devices, as well as towards non-volatile memory and neuromorphic devices with potential improvements in scalability, power consumption, switching speed, and compatibility with CMOS integration.The first part of this dissertation presents contributions towards high-yield 2DMs field- effect-transistors (FETs) fabrication using wafer-scale chemical vapor deposition (CVD) monolayer MoS2. This work provides valuable insight about metal contact processing, including extraction of Schottky barrier heights and Fermi-level pinning effects, for next- generation integrated electronic systems based on CVD-grown 2DMs.The second part introduces wafer-scale fabrication of memristor arrays with CVD- grown hexagonal boron nitride (h-BN) as the active switching layer. This work establishes the multi-state analog pulse programmability and presents the first experimental demonstration of dot-product computation and implementation of multi-variable stochastic linear regression on h-BN memristor hardware. This work extends beyond previous demonstrations of non-volatile resistive switching (NVRS) behavior in isolated h-BN memristors and paves the way for more sophisticated demonstrations of machine learning applications based on 2DMs.Finally, combining the benefits of CVD-grown 2DMs and graphene edge contacts, vertical h-BN memristors with ultra-small active areas are introduced through this research. These devices achieve low operating currents (high resistance), large RHRS/RLRS ratio, and enable three-dimensional (3D) integration (vertical stacking) for ultimate RRAM scalability. Moreover, they facilitate studying fundamental NVRS mechanisms of single conductive nano-filaments (CNFs) which was previously unattainable in planar devices. This way, single quantum step in conductance was experimentally observed, consistent with theorized atomically-constrained CNFs behavior associated with potential improvements in stability of NVRS operation. This is supported by measured improvements in retention of quantized conductance compared to other non-2DMs filamentary-based memristors.

ISBN: 9798381158151Subjects--Topical Terms:

649834
Electrical engineering.
Subjects--Index Terms:

Memristors

Two Dimensional Materials Based Memristors for In-Memory Computing and Neuromorphic Computing.
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500 $a Source: Dissertations Abstracts International, Volume: 85-06, Section: B.
500 $a Advisor: Sanchez Esqueda, Ivan.
502 $a Thesis (Ph.D.)--Arizona State University, 2023.
506 $a This item must not be sold to any third party vendors.
520 $a Modern Complementary-Metal-Oxide-Semiconductor (CMOS) technologies are facing critical challenges: scaling channel lengths below ~10 nm is hindered by significant transport degradation as bulk semiconductors (i.e., silicon) are thinned down, energy consumption is affected by short-channel effects and off-state leakage, and conventional von Neumann computing architectures face serious bottlenecks affecting performance and efficiency (energy consumption and throughput). Neuromorphic and/or in-memory computing architectures using resistive random-access memory (RRAM) crossbar arrays are promising candidates to mitigate these bottlenecks and to circumvent CMOS scaling challenges. Recently, emerging two dimensional materials (2DMs) are investigated towards ultra-scaled CMOS devices, as well as towards non-volatile memory and neuromorphic devices with potential improvements in scalability, power consumption, switching speed, and compatibility with CMOS integration.The first part of this dissertation presents contributions towards high-yield 2DMs field- effect-transistors (FETs) fabrication using wafer-scale chemical vapor deposition (CVD) monolayer MoS2. This work provides valuable insight about metal contact processing, including extraction of Schottky barrier heights and Fermi-level pinning effects, for next- generation integrated electronic systems based on CVD-grown 2DMs.The second part introduces wafer-scale fabrication of memristor arrays with CVD- grown hexagonal boron nitride (h-BN) as the active switching layer. This work establishes the multi-state analog pulse programmability and presents the first experimental demonstration of dot-product computation and implementation of multi-variable stochastic linear regression on h-BN memristor hardware. This work extends beyond previous demonstrations of non-volatile resistive switching (NVRS) behavior in isolated h-BN memristors and paves the way for more sophisticated demonstrations of machine learning applications based on 2DMs.Finally, combining the benefits of CVD-grown 2DMs and graphene edge contacts, vertical h-BN memristors with ultra-small active areas are introduced through this research. These devices achieve low operating currents (high resistance), large RHRS/RLRS ratio, and enable three-dimensional (3D) integration (vertical stacking) for ultimate RRAM scalability. Moreover, they facilitate studying fundamental NVRS mechanisms of single conductive nano-filaments (CNFs) which was previously unattainable in planar devices. This way, single quantum step in conductance was experimentally observed, consistent with theorized atomically-constrained CNFs behavior associated with potential improvements in stability of NVRS operation. This is supported by measured improvements in retention of quantized conductance compared to other non-2DMs filamentary-based memristors.
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650 4 $a Computer science. $3 523869
650 4 $a Nanoscience. $3 587832
653 $a Memristors
653 $a In-memory computing
653 $a Neuromorphic computing
653 $a Bulk semiconductors
653 $a Power consumption
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