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.

Dilute magnetic semiconductors (DMS), achieved through substitutional doping of spin-polarized transition metals into semiconducting systems, enable experimental modulation of spin dynamics in ways that hold great promise for novel magneto-electric or magneto-optical devices, especially for two-dimensional (2D) systems such as transition metal dichalcogenides that accentuate interactions and activate valley degrees of freedom. Practical applications of 2D magnetism will likely require room-temperature operation, air stability, and (for magnetic semiconductors) the ability to achieve optimal doping levels without dopant aggregation. Here, room-temperature ferromagnetic order obtained in semiconducting vanadium-doped tungsten disulfide monolayers produced by a reliable single-step film sulfidation method across an exceptionally wide range of vanadium concentrations, up to 12 at% with minimal dopant aggregation, is described. These monolayers develop p-type transport as a function of vanadium incorporation and rapidly reach ambipolarity. Ferromagnetism peaks at an intermediate vanadium concentration of˜2 at% and decreases for higher concentrations, which is consistent with quenching due to orbital hybridization at closer vanadium-vanadium spacings, as supported by transmission electron microscopy, magnetometry, and first-principles calculations. Room-temperature 2D-DMS provide a new component to expand the functional scope of van der Waals heterostructures and bring semiconducting magnetic 2D heterostructures into the realm of practical application.

Scalable substitutional doping of 2D transition metal dichalcogenides is a prerequisite to developing next-generation logic and memory devices based on 2D materials. To date, doping efforts are still nascent. Here, scalable growth and vanadium (V) doping of 2D WSe 2 at front-end-of-line and backend-of-line compatible temperatures of 800 and 400 °C, respectively, is reported. A combination of experimental and theoretical studies confirm that vanadium atoms substitutionally replace tungsten in WSe 2 , which results in p-type doping via the introduction of discrete defect levels that lie close to the valence band maxima. The p-type nature of the V dopants is further verified by constructed field-effect transistors, where hole conduction becomes dominant with increasing vanadium concentration. Hence, this study presents a method to precisely control the density of intentionally introduced impurities, which is indispensable in the production of electronic-grade wafer-scale extrinsic 2D semiconductors.

Artificial neural networks have demonstrated superiority over traditional computing architectures in tasks such as pattern classification and learning. However, they do not measure uncertainty in predictions, and hence they can make wrong predictions with high confidence, which can be detrimental for many mission-critical applications. In contrast, Bayesian neural networks (BNNs) naturally include such uncertainty in their model, as the weights are represented by probability distributions (e.g. Gaussian distribution). Here we introduce three-terminal memtransistors based on two-dimensional (2D) materials, which can emulate both probabilistic synapses as well as reconfigurable neurons. The cycle-to-cycle variation in the programming of the 2D memtransistor is exploited to achieve Gaussian random number generator-based synapses, whereas 2D memtransistor based integrated circuits are used to obtain neurons with hyperbolic tangent and sigmoid activation functions. Finally, memtransistor-based synapses and neurons are combined in a crossbar array architecture to realize a BNN accelerator for a data classification task.

Atomically thin, 2D, and semiconducting transition metal dichalcogenides (TMDs) are seen as potential candidates for complementary metal oxide semiconductor (CMOS) technology in future nodes. While high‐performance field effect transistors (FETs), logic gates, and integrated circuits (ICs) made from n‐type TMDs such as MoS2 and WS2 grown at wafer scale have been demonstrated, realizing CMOS electronics necessitates integration of large area p‐type semiconductors. Furthermore, the physical separation of memory and logic is a bottleneck of the existing CMOS technology and must be overcome to reduce the energy burden for computation. In this article, the existing limitations are overcome and for the first time, a heterogeneous integration of large area grown n‐type MoS2 and p‐type vanadium doped WSe2 FETs with non‐volatile and analog memory storage capabilities to achieve a non–von Neumann 2D CMOS platform is introduced. This manufacturing process flow allows for precise positioning of n‐type and p‐type FETs, which is critical for any IC development. Inverters and a simplified 2‐input‐1‐output multiplexers and neuromorphic computing primitives such as Gaussian, sigmoid, and tanh activation functions using this non–von Neumann 2D CMOS platform are also demonstrated. This demonstration shows the feasibility of heterogeneous integration of wafer scale 2D materials.