All WSe2 1T1R resistive RAM cell for future monolithic 3D embedded memory integration

Sivan, Maheswari; Li, Yida; Veluri, Hasita; Zhao, Yunshan; Tang, Baoshan; Wang, Xinghua; Zamburg, Evgeny; Leong, Jin Feng; Niu, Jessie Xuhua; Chand, Umesh; Thean, Aaron

doi:10.1038/s41467-019-13176-4

Cited by 123 publications

(141 citation statements)

References 66 publications

(64 reference statements)

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“…Graphene and related two-dimensional (2D) materials are a family of exciting materials, which are as small as one to three atoms in vertical direction, but extremely large in horizontal space [1][2][3][4][5]. These systems have attracted significant attention for nanoelectronics and optoelectronics [6][7][8][9][10], non-von Neumann architecture computing [11,12], hybrid flexible and stretchable electronics [13][14][15], and many other applications. As a consequence of their extremely high market value, 2D materials are very attractive to many industries.…”

Section: Introductionmentioning

confidence: 99%

RETRACTED: Artificial Intelligence Algorithm Enabled Industrial-Scale Graphene Characterization

2020

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No characterization method is available to quickly perform quality inspection of 2D materials produced on an industrial scale. This hinders the adoption of 2D materials for product manufacturing in many industries. Here, we report an artificial-intelligence-assisted Raman analysis to quickly probe the quality of centimeter-large graphene samples in a non-destructive manner. Chemical vapor deposition of graphene is devised in this work such that two types of samples were obtained: layer-plus-islands and layer-by-layer graphene films, at centimeter scales. Using these samples, we implemented and integrated an unsupervised learning algorithm with an automated Raman spectroscopy to precisely cluster 20,250 and 18,000 Raman spectra collected from layer-plus-islands and layer-by-layer graphene films, respectively, into five and two clusters. Each cluster represents graphene patches with different layer numbers and stacking orders. For instance, the two clusters detected in layer-by-layer graphene films represent monolayer and bilayer graphene based on their Raman fingerprints. Our intelligent Raman analysis is fully automated, with no human operation involved, is highly reliable (99.95% accuracy), and can be generalized to other 2D materials, paving the way towards industrialization of 2D materials for various applications in the future.

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Section: Introductionmentioning

confidence: 99%

RETRACTED: Artificial Intelligence Algorithm Enabled Industrial-Scale Graphene Characterization

2020

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“…However, previous relevant studies, including those on electronic and phonon transport, have focused on micrometer-sized TMDCs obtained via mechanical exfoliation or CVD. [43,[46][47][48] To the best of our knowledge, no experimental studies have been conducted on large-area ML WSe 2 of the size 4 × 6 mm 2 as an intermediate layer in LSSE spin transport. To achieve this, we used a novel PVD method with WSe 2 powder at 650 °C for growing millimeter-scale large-area ML WSe 2 (see Experimental and Material Characterization sections).…”

Section: Discussionmentioning

confidence: 99%

Enhanced Spin Seebeck Effect in Monolayer Tungsten Diselenide Due to Strong Spin Current Injection at Interface

Lee

Kikkawa

et al. 2020

Adv Funct Materials

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The spin current is significantly limited by the spin‐orbit interaction strength, material quality, and spin‐mixing conductance at material interfaces. Such limitations lead to spin current decay at the interfaces, which severely hinders potential applications in spin‐current‐generating thermoelectric devices. Thus, methodical studies on the enhancement of spin currents are indispensable. Herein, a novel approach for enhancing the spin current injected into a normal metal, Pt, using interface effects with a ferromagnetic insulator, yttrium iron garnet (YIG), is demonstrated. This is accomplished by inserting atomically thin monolayer (ML), tungsten diselenide (WSe2) between Pt and YIG layers. A comparative study of longitudinal spin Seebeck effect (LSSE) measurements is conducted. Two types of ML WSe2 (continuous and large‐area ML WSe2 and isolated ML WSe2 flakes) are used as intermediate layers on YIG film. Notably, the insertion of ML WSe2 between the Pt and YIG layers significantly enhances the thermopower, VLSSE/ΔT by a factor of approximately 5.6 compared with that of the Pt/YIG reference sample. This enhancement in the measured LSSE voltages in the Pt/ML WSe2/YIG trilayer can be explained by the increased spin‐to‐charge conversion at the interface owing to the large spin‐orbit coupling and improved spin mixing conductance with the ML WSe2 intermediate layer.

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“…Volatile switching and nonvolatile switching behaviors can even coexist and interconvert in the same device with appropriate conditions. [13][14][15][16] A great number of materials have been explored as active layer and electrodes for volatile memristors in the past few decades (see Table 1). The active layer materials Ruopeng Wang received a B.S.…”

Section: Methodsmentioning

confidence: 99%

Recent Advances of Volatile Memristors: Devices, Mechanisms, and Applications

Wang

Yang

Mao

et al. 2020

Advanced Intelligent Systems

120

113

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Due to the rapid development of artificial intelligence (AI) and internet of things (IoTs), neuromorphic computing and hardware security are becoming more and more important. The volatile memristors, which feature spontaneous decay of device conductance, own the distinct combination of high similarity to the biological neurons and synapses and unique physical mechanisms. They are excellent candidates for mimicking the synaptic functions and ideal randomness source of the entropy for hardware‐based security. Herein, the recent advances of volatile memristors in devices, mechanisms, and application aspects are summarized. First, a brief introduction is presented to describe the switching type, materials, and temporal response of volatile memristors. Second, the volatile switching mechanisms are discussed and grouped into ion effects, thermal effects, and electrical effects. Third, attention is focused on the applications of volatile memristors for access devices, neuromorphic computing (artificial neurons and synapses), and hardware security (true random number generators and physical unclonable functions). Finally, major challenges and future outlook of volatile memristors for neuromorphic computing and hardware security are discussed.

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All WSe2 1T1R resistive RAM cell for future monolithic 3D embedded memory integration

Cited by 123 publications

References 66 publications

RETRACTED: Artificial Intelligence Algorithm Enabled Industrial-Scale Graphene Characterization

RETRACTED: Artificial Intelligence Algorithm Enabled Industrial-Scale Graphene Characterization

Enhanced Spin Seebeck Effect in Monolayer Tungsten Diselenide Due to Strong Spin Current Injection at Interface

Recent Advances of Volatile Memristors: Devices, Mechanisms, and Applications

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