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Abstract 2D layered semiconductors have attracted considerable attention for beyond‐Si complementary metal‐oxide‐semiconductor (CMOS) technologies. They can be prepared into ultrathin channel materials toward ultrascaled device architectures, including double‐gate field‐effect‐transistors (DGFETs). This work presents an experimental analysis of DGFETs constructed from chemical vapor deposition (CVD)‐grown monolayer (1L) molybdenum disulfide (MoS₂) with atomic layer deposition (ALD) of hafnium oxide (HfO₂) high‐k gate dielectrics (top and bottom). This extends beyond previous studies of DGFETs based mostly on exfoliated (few‐nm thick) MoS₂flakes, and advances toward large‐area wafer‐scale processing. Here, significant improvements in performance are obtained with DGFETs (i.e., improvements in ON/OFF ratio, ON‐state current, sub‐threshold swing, etc.) compared to single top‐gate FETs. In addition to multi‐gate device architectures (e.g., DGFETs), the scaling of the equivalent oxide thickness (EOT) is crucial toward improved electrostatics required for next‐generation transistors. However, the impact of EOT scaling on the characteristics of CVD‐grown MoS₂DGFETs remains largely unexplored. Thus, this work studies the impact of EOT scaling on subthreshold swing (SS) and gate hysteresis using current–voltage (I–V) measurements with varying sweep rates. The experimental analysis and results elucidate the basic mechanisms responsible for improvements in CVD‐grown 1L‐MoS₂DGFETs compared to standard top‐gate FETs. 

Abstract This work reports on the hardware implementation of analog dot-product operation on arrays of 2D hexagonal boron nitride (h-BN) memristors. This extends beyond previous work that studied isolated device characteristics towards the application of analog neural network accelerators based on 2D memristor arrays. The wafer-level fabrication of the memristor arrays is enabled by large-area transfer of CVD-grown few-layer (8 layers) h-BN films. Individual devices achieve an on/off ratio of >10, low voltage operation (~0.5 Vset/Vreset), good endurance (>6,000 programming steps), and good retention (>104 s). The dot-product operation shows excellent linearity and repeatability, with low read energy consumption (~200 aJ to 20 fJ per operation), with minimal error and deviation over various measurement cycles. Moreover, we present the implementation of a stochastic logistic regression algorithm in 2D h-BN memristor hardware for the classification of noisy images. The promising resistive switching characteristics, performance of dot-product computation, and successful demonstration of logistic regression in h-BN memristors signify an important step towards the integration of 2D materials for next-generation neuromorphic computing systems. 

Abstract Recent studies of resistive switching devices with hexagonal boron nitride (h-BN) as the switching layer have shown the potential of two-dimensional (2D) materials for memory and neuromorphic computing applications. The use of 2D materials allows scaling the resistive switching layer thickness to sub-nanometer dimensions enabling devices to operate with low switching voltages and high programming speeds, offering large improvements in efficiency and performance as well as ultra-dense integration. These characteristics are of interest for the implementation of neuromorphic computing and machine learning hardware based on memristor crossbars. However, existing demonstrations of h-BN memristors focus on single isolated device switching properties and lack attention to fundamental machine learning functions. This paper demonstrates the hardware implementation of dot product operations, a basic analog function ubiquitous in machine learning, using h-BN memristor arrays. Moreover, we demonstrate the hardware implementation of a linear regression algorithm on h-BN memristor arrays. 

Abstract This paper provides comprehensive experimental analysis relating to improvements in the two-dimensional (2D) p-type metal–oxide–semiconductor (PMOS) field effect transistors (FETs) by pure van der Waals (vdW) contacts on few-layer tungsten diselenide (WSe₂) with high-k metal gate (HKMG) stacks. Our analysis shows that standard metallization techniques (e.g., e-beam evaporation at moderate pressure ~ 10^–5 torr) results in significant Fermi-level pinning, but Schottky barrier heights (SBH) remain small (< 100 meV) when using high work function metals (e.g., Pt or Pd). Temperature-dependent analysis uncovers a more dominant contribution to contact resistance from the channel access region and confirms significant improvement through less damaging metallization techniques (i.e., reduced scattering) combined with strongly scaled HKMG stacks (enhanced carrier density). A clean contact/channel interface is achieved through high-vacuum evaporation and temperature-controlled stepped deposition providing large improvements in contact resistance. Our study reports low contact resistance of 5.7 kΩ-µm, with on-state currents of ~ 97 µA/µm and subthreshold swing of ~ 140 mV/dec in FETs with channel lengths of 400 nm. Furthermore, theoretical analysis using a Landauer transport ballistic model for WSe₂SB-FETs elucidates the prospects of nanoscale 2D PMOS FETs indicating high-performance (excellent on-state current vs subthreshold swing benchmarks) towards the ultimate CMOS scaling limit. 

Abstract Previous work that studied hexagonal boron nitride (h‐BN) memristor DC resistive‐switching characteristics is extended to include an experimental understanding of their dynamic behavior upon programming or synaptic weight update. The focus is on the temporal resistive switching response to driving stimulus (programming voltage pulses) effecting conductance updates during training in neural network crossbar implementations. Test arrays are fabricated at the wafer level, enabled by the transfer of CVD‐grown few‐layer (8 layer) or multi‐layer (18 layer) h‐BN films. A comprehensive study of their temporal response under various conditions–voltage pulse amplitude, edge rate (pulse rise/fall times), and temperature–provides new insights into the resistive switching process toward optimized devices and improvements in their implementation of artificial neural networks. The h‐BN memristors can achieve multi‐state operation through ultrafast pulsed switching (< 25 ns) with high energy efficiency (≈10 pJ pulse⁻¹). 

Abstract Chemical vapor deposition (CVD)-grown monolayer (ML) molybdenum disulfide (MoS 2 ) is a promising material for next-generation integrated electronic systems due to its capability of high-throughput synthesis and compatibility with wafer-scale fabrication. Several studies have described the importance of Schottky barriers in analyzing the transport properties and electrical characteristics of MoS 2 field-effect-transistors (FETs) with metal contacts. However, the analysis is typically limited to single devices constructed from exfoliated flakes and should be verified for large-area fabrication methods. In this paper, CVD-grown ML MoS 2 was utilized to fabricate large-area (1 cm × 1 cm) FET arrays. Two different types of metal contacts (i.e. Cr/Au and Ti/Au) were used to analyze the temperature-dependent electrical characteristics of ML MoS 2 FETs and their corresponding Schottky barrier characteristics. Statistical analysis provides new insight about the properties of metal contacts on CVD-grown MoS 2 compared to exfoliated samples. Reduced Schottky barrier heights (SBH) are obtained compared to exfoliated flakes, attributed to a defect-induced enhancement in metallization of CVD-grown samples. Moreover, the dependence of SBH on metal work function indicates a reduction in Fermi level pinning compared to exfoliated flakes, moving towards the Schottky–Mott limit. Optical characterization reveals higher defect concentrations in CVD-grown samples supporting a defect-induced metallization enhancement effect consistent with the electrical SBH experiments. 

The development of innovative antimicrobial materials is crucial in thwarting infectious diseases caused by microbes, as drug-resistant pathogens are increasing in both number and capacity to detoxify the antimicrobial drugs used today. An ideal antimicrobial material should inhibit a wide variety of bacteria in a short period of time, be less or not toxic to normal cells, and the fabrication or synthesis process should be cheap and easy. We report a one-step microwave-assisted hydrothermal synthesis of mixed composite CuxFeyOz (Fe2O3/Cu2O/CuO/CuFe2O) nanoparticles (NPs) as an excellent antimicrobial material. The 1 mg/mL CuxFeyOz NPs with the composition 36% CuFeO2, 28% Cu2O and 36% Fe2O3 have a general antimicrobial activity greater than 5 log reduction within 4 h against nine important human pathogenic bacteria (including drug-resistant bacteria as well as Gram-positive and Gram-negative strains). For example, they induced a >9 log reduction in Escherichia coli B viability after 15 min of incubation, and an ~8 log reduction in multidrug-resistant Klebsiella pneumoniae after 4 h incubation. Cytotoxicity tests against mouse fibroblast cells showed about 74% viability when exposed to 1 mg/mL CuxFeyOz NPs for 24 h, compared to the 20% viability for 1 mg/mL pure Cu2O NPs synthesized by the same method. These results show that the CuxFeyOz composite NPs are a highly efficient, low-toxicity and cheap antimicrobial material that has promising potential for applications in medical and food safety.