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Metalorganic chemical vapor deposition (MOCVD) is a promising technique for wafer-scale synthesis of MoS2 monolayers for 2D field-effect transistors (2D-FETs) and related devices. Epitaxial growth of MoS2 on sapphire provides films that are crystallographically well-oriented but typically contain low-angle grain boundaries (e.g., mirror twins), voids, and other defects depending on growth conditions and substrate characteristics. In this study, we investigate microstructure, optical properties, and field-effect characteristics of wafer-scale MoS2 monolayers grown by MOCVD on c-plane sapphire over a narrow window of growth temperatures (900–1000 °C). The density of low-angle grain boundaries in the MoS2 monolayer was found to decrease dramatically from 50% areal coverage for films grown at 900 °C to 5% at 1000 °C. This decrease in low-angle grain boundary density is correlated with an increase in the room-temperature photoluminescence intensity of A excitons and a decrease in the full-width-half maximum (FWHM) of the Raman A1g peak, which are typically indicative of a general reduction in defects in MoS2. However, the best transport properties (e.g., mean field-effect mobility mFE = 17.3 cm2/V s) were obtained in MoS2 monolayers grown at an intermediate temperature of 950 °C. It was found that as the growth temperature increased, small regions bound by high-angle boundaries begin to appear within the monolayer and increase in areal coverage, from ∼2% at 900 °C to ∼5% at 950 °C to ∼10% at 1000 °C. The growth temperature of 950 °C, therefore, provides an intermediate condition where the combined effects of low-angle and high-angle boundaries are minimized. The results of this study provide guidance on MOCVD growth and characterization that can be used to further optimize the performance of MoS2 2D-FETs.

 

Abstract In the emerging era of the internet of things (IoT), ubiquitous sensors continuously collect, consume, store, and communicate a huge volume of information which is becoming increasingly vulnerable to theft and misuse. Modern software cryptosystems require extensive computational infrastructure for implementing ciphering algorithms, making them difficult to be adopted by IoT edge sensors that operate with limited hardware resources and at low energy budgets. Here we propose and experimentally demonstrate an “all-in-one” 8 × 8 array of robust, low-power, and bio-inspired crypto engines monolithically integrated with IoT edge sensors based on two-dimensional (2D) memtransistors. Each engine comprises five 2D memtransistors to accomplish sensing and encoding functionalities. The ciphered information is shown to be secure from an eavesdropper with finite resources and access to deep neural networks. Our hardware platform consists of a total of 320 fully integrated monolayer MoS 2 -based memtransistors and consumes energy in the range of hundreds of picojoules and offers near-sensor security. 

Abstract Bayesian networks (BNs) find widespread application in many real-world probabilistic problems including diagnostics, forecasting, computer vision, etc. The basic computing primitive for BNs is a stochastic bit (s-bit) generator that can control the probability of obtaining ‘1’ in a binary bit-stream. While silicon-based complementary metal-oxide-semiconductor (CMOS) technology can be used for hardware implementation of BNs, the lack of inherent stochasticity makes it area and energy inefficient. On the other hand, memristors and spintronic devices offer inherent stochasticity but lack computing ability beyond simple vector matrix multiplication due to their two-terminal nature and rely on extensive CMOS peripherals for BN implementation, which limits area and energy efficiency. Here, we circumvent these challenges by introducing a hardware platform based on 2D memtransistors. First, we experimentally demonstrate a low-power and compact s-bit generator circuit that exploits cycle-to-cycle fluctuation in the post-programmed conductance state of 2D memtransistors. Next, the s-bit generators are monolithically integrated with 2D memtransistor-based logic gates to implement BNs. Our findings highlight the potential for 2D memtransistor-based integrated circuits for non-von Neumann computing applications. 

Abstract 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. 

Abstract Atomically thin transition metal dichalcogenides (TMDs), like MoS 2 with high carrier mobilities and tunable electron dispersions, are unique active material candidates for next generation opto-electronic devices. Previous studies on ion irradiation show great potential applications when applied to two-dimensional (2D) materials, yet have been limited to micron size exfoliated flakes or smaller. To demonstrate the scalability of this method for industrial applications, we report the application of relatively low power (50 keV) 4 He + ion irradiation towards tuning the optoelectronic properties of an epitaxially grown continuous film of MoS 2 at the wafer scale, and demonstrate that precise manipulation of atomistic defects can be achieved in TMD films using ion implanters. The effect of 4 He + ion fluence on the PL and Raman signatures of the irradiated film provides new insights into the type and concentration of defects formed in the MoS 2 lattice, which are quantified through ion beam analysis. PL and Raman spectroscopy indicate that point defects are generated without causing disruption to the underlying lattice structure of the 2D films and hence, this technique can prove to be an effective way to achieve defect-mediated control over the opto-electronic properties of MoS 2 and other 2D materials.