Multi-scale modeling of gas-phase reactions in metal-organic chemical vapor deposition growth of WSe2

Xuan, Yuan; Jain, Abhishek; Zafar, Suhaib; Lotfi, Roghayyeh; Nayir, Nadire; Wang, Yuanxi; Choudhury, Tanushree H.; Wright, Samuel; Feraca, John; Rosenbaum, Leonard; Redwing, Joan M.; Crespi, Vincent H.

doi:10.1016/j.jcrysgro.2019.125247

Cited by 79 publications

(96 citation statements)

References 46 publications

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“…Figure 1 summarizes the growth, structural, and optical characterization of the MOCVD grown MoS 2 and WS 2 . Figure 1a shows the schematic of the MOCVD system, comprising of a cold-wall horizontal reactor with an inductively heated graphite susceptor equipped with wafer rotation as previously described 27 . Molybdenum hexacarbonyl (Mo(CO) 6 ) and tungsten hexacarbonyl (W(CO) 6 ) were used as metal precursors, while hydrogen sulfide (H 2 S) was the chalcogen source with H 2 as the carrier gas.…”

Section: Resultsmentioning

confidence: 99%

Benchmarking monolayer MoS2 and WS2 field-effect transistors

et al. 2021

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Here we benchmark device-to-device variation in field-effect transistors (FETs) based on monolayer MoS2 and WS2 films grown using metal-organic chemical vapor deposition process. Our study involves 230 MoS2 FETs and 160 WS2 FETs with channel lengths ranging from 5 μm down to 100 nm. We use statistical measures to evaluate key FET performance indicators for benchmarking these two-dimensional (2D) transition metal dichalcogenide (TMD) monolayers against existing literature as well as ultra-thin body Si FETs. Our results show consistent performance of 2D FETs across 1 × 1 cm2 chips owing to high quality and uniform growth of these TMDs followed by clean transfer onto device substrates. We are able to demonstrate record high carrier mobility of 33 cm2 V−1 s−1 in WS2 FETs, which is a 1.5X improvement compared to the best reported in the literature. Our experimental demonstrations confirm the technological viability of 2D FETs in future integrated circuits.

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Section: Resultsmentioning

confidence: 99%

Benchmarking monolayer MoS2 and WS2 field-effect transistors

et al. 2021

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“…An inductively heated graphite susceptor equipped with wafer rotation in a cold-wall horizontal reactor was used to achieve uniform monolayer deposition (schematic in Fig. 1a) as previously described 54 . Molybdenum hexacarbonyl (Mo(CO) 6 ) and hydrogen sulfide (H 2 S) were used as precursors.…”

Section: Synthesis Of Monolayer Mos 2 Monolayer Mos 2 Was Depositedmentioning

confidence: 99%

Stochastic resonance in MoS2 photodetector

et al. 2020

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In this article, we adopt a radical approach for next generation ultra-low-power sensor design by embracing the evolutionary success of animals with extraordinary sensory information processing capabilities that allow them to survive in extreme and resource constrained environments. Stochastic resonance (SR) is one of those astounding phenomena, where noise, which is considered detrimental for electronic circuits and communication systems, plays a constructive role in the detection of weak signals. Here, we show SR in a photodetector based on monolayer MoS 2 for detecting ultra-low-intensity subthreshold optical signals from a distant light emitting diode (LED). We demonstrate that weak periodic LED signals, which are otherwise undetectable, can be detected by a MoS 2 photodetector in the presence of a finite and optimum amount of white Gaussian noise at a frugal energy expenditure of few tens of nano-Joules. The concept of SR is generic in nature and can be extended beyond photodetector to any other sensors.

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“…It was deposited on epi-ready 2" c-sapphire substrate by metalorganic chemical vapor deposition (MOCVD). An inductively heated graphite susceptor equipped with wafer rotation in a cold-wall horizontal reactor was used to achieve uniform monolayer deposition as previously described [65].…”

Section: Methodsmentioning

confidence: 99%

A Bio-inspired and Low-power 2D Machine Vision with Adaptive Machine Learning and Forgetting

Dodda¹,

Jayachandran²,

Radhakrishnan³

et al. 2021

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Natural intelligence has many dimensions, and in animals, learning about the environment and making behavioral changes are some of its manifestations. In primates vision plays a critical role in learning. The underlying biological neural networks contain specialized neurons and synapses which not only sense and process the visual stimuli but also learns and adapts, with remarkable energy efficiency. Forgetting also plays an active role in learning. Mimicking the adaptive neurobiological mechanisms for seeing, learning, and forgetting can, therefore, accelerate the development of artificial intelligence (AI) and bridge the massive energy gap that exists between AI and biological intelligence. Here we demonstrate a bio-inspired machine vision based on large area grown monolayer 2D phototransistor array integrated with analog, non-volatile, and programmable memory gate-stack that not only enables direct learning, and unsupervised relearning from the visual stimuli but also offers learning adaptability under photopic (bright-light), scotopic (low-light), as well as noisy illumination conditions at miniscule energy expenditure. In short, our “all-in-one” hardware vision platform combines “sensing”, “computing” and “storage” not only to overcome the von Neumann bottleneck of conventional complementary metal oxide semiconductor (CMOS) technology but also to eliminate the need for peripheral circuits and sensors.

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Multi-scale modeling of gas-phase reactions in metal-organic chemical vapor deposition growth of WSe2

Cited by 79 publications

References 46 publications

Benchmarking monolayer MoS2 and WS2 field-effect transistors

Benchmarking monolayer MoS2 and WS2 field-effect transistors

Stochastic resonance in MoS2 photodetector

A Bio-inspired and Low-power 2D Machine Vision with Adaptive Machine Learning and Forgetting

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