Zhenyu Wang

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https://people.epfl.ch/zhenyu.wang?lang=fr

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Electrical spectroscopy of defect states and their hybridization in monolayer MoS2

Ahmet Avsar, Kristians Cernevics, Cheol Yeon Cheon, Juan Francisco Gonzalez Marin, Hyungoo Ji, Andras Kis, Mukesh Kumar Tripathi, Zhenyu Wang, Oleg Yazyev, Yanfei Zhao

Defects in solids are unavoidable and can create complex electronic states that can significantly influence the electrical and optical properties of semiconductors. With the rapid progress in the integration of 2D semiconductors in practical devices, it is imperative to understand and characterize the influence of defects in this class of materials. Here, we examine the electrical response of defect filling and emission using deep level transient spectroscopy (DLTS) and reveal defect states and their hybridization in a monolayer MOCVD-grown material deposited on CMOS-compatible substrates. Supported by aberration-corrected STEM imaging and theoretical calculations, we find that neighboring sulfur vacancy pairs introduce additional shallow trap states via hybridization of individual vacancy levels. Even though such vacancy pairs only represent ~10% of the total defect concentration, they can have a substantial influence on the off currents and switching slopes of field-effect transistors based on 2D semiconductors. Our technique, which can quantify the energy states of different defects and their interactions, allows rapid and nondestructive electrical characterization of defect states important for the defect engineering of 2D semiconductors.

2023

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Substitutional p‐type Doping in NbS2‐MoS2 Lateral Heterostructures Grown by MOCVD

Hyungoo Ji, Andras Kis, Guilherme Migliato Marega, Aleksandra Radenovic, Mukesh Kumar Tripathi, Zhenyu Wang, Yanfei Zhao

2023

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Machine learning and signal processing on the edge are poised to influence our everyday lives with devices that will learn and infer from data generated by smart sensors and other devices for the Internet of Things. The next leap toward ubiquitous electronics requires increased energy efficiency of processors for specialized data-driven applications. Here, we show how an in-memory processor fabricated using a two-dimensional materials platform can potentially outperform its silicon counterparts in both standard and nontraditional Von Neumann architectures for artificial neural networks. We have fabricated a flash memory array with a two-dimensional channel using wafer-scale MoS2. Simulations and experiments show that the device can be scaled down to sub-micrometer channel length without any significant impact on its memory performance and that in simulation a reasonable memory window still exists at sub-50 nm channel lengths. Each device conductance in our circuit can be tuned with a 4-bit precision by closed-loop programming. Using our physical circuit, we demonstrate seven-segment digit display classification with a 91.5% accuracy with training performed ex situ and transferred from a host. Further simulations project that at a system level, the large memory arrays can perform AlexNet classification with an upper limit of 50 000 TOpS/W, potentially outperforming neural network integrated circuits based on double-poly CMOS technology.

2022

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