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Abstract This work reports experimental demonstrations of reversible crystalline phase transition in ultrathin molybdenum ditelluride (MoTe₂) controlled by thermal and mechanical mechanisms on the van der Waals (vdW) nanoelectromechanical systems (NEMS) platform, with hexagonal boron nitride encapsulated MoTe₂structure residing on top of graphene layer. Benefiting from very efficient electrothermal heating and straining effects in the suspended vdW heterostructures, MoTe₂phase transition is triggered by rising temperature and strain level. Raman spectroscopy monitors the MoTe₂crystalline phase signatures in situ and clearly records reversible phase transitions between hexagonal 2H (semiconducting) and monoclinic 1T′ (metallic) phases. Combined with Raman thermometry, precisely measured nanomechanical resonances of the vdW devices enable the determination and monitoring of the strain variations as temperature is being regulated by electrothermal control. These results not only deepen the understanding of MoTe₂phase transition, but also demonstrate a novel platform for engineering MoTe₂phase transition and multiphysical devices. 

Abstract Memory technologies and applications implemented fully or partially using emerging 2D materials have attracted increasing interest in the research community in recent years. Their unique characteristics provide new possibilities for highly integrated circuits with superior performances and low power consumption, as well as special functionalities. Here, an overview of progress in 2D‐material‐based memory technologies and applications on the circuit level is presented. In the material growth and fabrication aspects, the advantages and disadvantages of various methods for producing large‐scale 2D memory devices are discussed. Reports on 2D‐material‐based integrated memory circuits, from conventional dynamic random‐access memory, static random‐access memory, and flash memory arrays, to emerging memristive crossbar structures, all the way to 3D monolithic stacking architecture, are systematically reviewed. Comparisons between experimental implementations and theoretical estimations for different integration architectures are given in terms of the critical parameters in 2D memory devices. Attempts to use 2D memory arrays for in‐memory computing applications, mostly on logic‐in‐memory and neuromorphic computing, are summarized here. Finally, challenges that impede the large‐scale applications of 2D‐material‐based memory are reviewed, and perspectives on possible approaches toward a more reliable system‐level fabrication are also given, hopefully shedding some light on future research. 

Edge devices face challenges when implementing deep neural networks due to constraints on their computational resources and power consumption. Fuzzy logic systems can potentially provide more efficient edge implementations due to their compactness and capacity to manage uncertain data. However, their hardware realization remains difficult, primarily because implementing reconfigurable membership function generators using conventional technologies requires high circuit complexity and power consumption. Here we report a multigate van der Waals interfacial junction transistor based on a molybdenum disulfide/graphene heterostructure that can generate tunable Gaussian-like and π-shaped membership functions. By integrating these generators with peripheral circuits, we create a reconfigurable fuzzy controller hardware capable of nonlinear system control. This fuzzy logic system can also be integrated with a few-layer convolution neural network to form a fuzzy neural network with enhanced performance in image segmentation. 

We introduce a hybrid model that synergistically combines machine learning (ML) with semiconductor device physics to simulate nanoscale transistors. This approach integrates a physics-based ballistic transistor model with an ML model that predicts ballisticity, enabling flexibility to interface the model with device data. The inclusion of device physics not only enhances the interpretability of the ML model but also streamlines its training process, reducing the necessity for extensive training data. The model's effectiveness is validated on both silicon nanotransistors and carbon nanotube FETs, demonstrating high model accuracy with a simplified ML component. We assess the impacts of various ML models—Multilayer Perceptron (MLP), Recurrent Neural Network (RNN), and RandomForestRegressor (RFR)—on predictive accuracy and training data requirements. Notably, hybrid models incorporating these components can maintain high accuracy with a small training dataset, with the RNN-based model exhibiting better accuracy compared to the MLP and RFR models. The trained hybrid model provides significant speedup compared to device simulations, and can be applied to predict circuit characteristics based on the modeled nanotransistors. 

Recent technology development of logic devices based on 2-D semiconductors such as MoS2, WS2, and WSe2 has triggered great excitement, paving the way to practical applications. Making low-resistance p-type contacts to 2-D semiconductors remains a critical challenge. The key to addressing this challenge is to find high-work function metallic materials which also introduce minimal metal-induced gap states (MIGSs) at the metal/semiconductor interface. In this work, we perform a systematic computational screening of novel metallic materials and their heterojunctions with monolayer WSe2 based on ab initio density functional theory and quantum device simulations. Two contact strategies, van der Waals (vdW) metallic contact and bulk semimetallic contact, are identified as promising solutions to achieving Schottky-barrier-free and low-contact-resistance p-type contacts for WSe2 p-type field-effect transistor (pFETs). Good candidates of p-type contact materials are found based on our screening criteria, including 1H-NbS2, 1H-TaS2, and 1T-TiS2 in the vdW metal category, as well as Co3Sn2S2 and TaP in the bulk semimetal category. Simulations of these new p-type contact materials suggest reduced MIGS, less Fermi-level pinning effect, negligible Schottky barrier height and small contact resistance (down to 20 Ωμm )