Device simulation study of multilayer MoS2Schottky barrier field-effect transistors.

Molybdenum disulfide (MoS2) is a representative two-dimensional layered transition-metal dichalcogenide semiconductor. Layer-number-dependent electronic properties are attractive in the development of nanomaterial-based electronics for a wide range of applications including sensors, switches, and amplifiers. MoS2field-effect transistors (FETs) have been studied as promising future nanoelectronic devices with desirable features of atomic-level thickness and high electrical properties. When a naturallyn-doped MoS2is contacted with metals, a strong Fermi-level pinning effect adjusts a Schottky barrier and influences its electronic characteristics significantly. In this study, we investigate multilayer MoS2Schottky barrier FETs (SBFETs), emphasizing the metal-contact impact on device performance via computational device modeling. We find thatp-type MoS2SBFETs may be built with appropriate metals and gate voltage control. Furthermore, we propose ambipolar multilayer MoS2SBFETs with asymmetric metal electrodes, which exhibit gate-voltage dependent ambipolar transport behavior through optimizing metal contacts in MoS2device. Introducing a dual-split gate geometry, the MoS2SBFETs can further operate in four distinct configurations:p - p,n - n,p - n, andn - p. Electrical characteristics are calculated, and improved performance of a high rectification ratio can be feasible as an attractive feature for efficient electrical and photonic devices.

High-Performance Mid-IR to Deep-UV van der Waals Photodetectors Capable of Local Spectroscopy at Room Temperature.

The ability to perform broadband optical spectroscopy with subdiffraction-limit resolution is highly sought-after for a wide range of critical applications. However, sophisticated near-field techniques are currently required to achieve this goal. We bypass this challenge by demonstrating an extremely broadband photodetector based on a two-dimensional (2D) van der Waals heterostructure that is sensitive to light across over a decade in energy from the mid-infrared (MIR) to deep-ultraviolet (DUV) at room temperature. The devices feature high detectivity (>109 cm Hz1/2 W-1) together with high bandwidth (2.1 MHz). The active area can be further miniaturized to submicron dimensions, far below the diffraction limit for the longest detectable wavelength of 4.1 µm, enabling such devices for facile measurements of local optical properties on atomic-layer-thickness samples placed in close proximity. This work can lead to the development of low-cost and high-throughput photosensors for hyperspectral imaging at the nanoscale.

Gate-Tunable Multiband van der Waals Photodetector and Polarization Sensor.

A single photodetector with tunable detection wavelengths and polarization sensitivity can potentially be harnessed for diverse optical applications ranging from imaging and sensing to telecommunications. Such a device will require the combination of multiple material systems with different structures, band gaps, and photoelectrical responses, which is extremely difficult to engineer using traditional epitaxial films. Here, we develop a multifunctional and high-performance photosensor using all van der Waals materials. The device features a gate-tunable spectral response that is switchable between near-infrared/visible and short-/midwave infrared, as well as broad-band operation, at room temperature. The linear polarization sensitivity in the telecommunication O-band can also be directly modulated between horizontal, vertical, and nonpolarizing modes. These effects originate from the balance of photocurrent generation in two of the active layers that can be manipulated by an electric field. The photodetector features high detectivity (>109 cmHz1/2W-1) together with fast operation speed (∼1 MHz) and can be further exploited for dual visible and infrared imaging.

Material-Inherent Noise Sources in Quantum Information Architecture.

NISQ is a representative keyword at present as an acronym for "noisy intermediate-scale quantum", which identifies the current era of quantum information processing (QIP) technologies. QIP science and technologies aim to accomplish unprecedented performance in computation, communications, simulations, and sensing by exploiting the infinite capacity of parallelism, coherence, and entanglement as governing quantum mechanical principles. For the last several decades, quantum computing has reached to the technology readiness level 5, where components are integrated to build mid-sized commercial products. While this is a celebrated and triumphant achievement, we are still a great distance away from quantum-superior, fault-tolerant architecture. To reach this goal, we need to harness technologies that recognize undesirable factors to lower fidelity and induce errors from various sources of noise with controllable correction capabilities. This review surveys noisy processes arising from materials upon which several quantum architectures have been constructed, and it summarizes leading research activities in searching for origins of noise and noise reduction methods to build advanced, large-scale quantum technologies in the near future.

Deep neural network analysis models for complex random telegraph signals.

Time-fluctuating signals are ubiquitous and diverse in many physical, chemical, and biological systems, among which random telegraph signals (RTSs) refer to a series of instantaneous switching events between two discrete levels from single-particle movements. A reliable RTS analysis is a crucial prerequisite to identify underlying mechanisms related to device performance and sensitivity. When numerous levels are involved, complex patterns of multilevel RTSs occur and make their quantitative analysis exponentially difficult, hereby systematic approaches are often elusive. In this work, we present a three-step analysis protocol via progressive knowledge-transfer, where the outputs of the early step are passed onto a subsequent step. Especially, to quantify complex RTSs, we resort to three deep neural network architectures whose trained models can process raw temporal data directly. We furthermore demonstrate the model accuracy extensively with a large dataset of different RTS types in terms of additional background noise types and amplitude size. Our protocol offers structured schemes to extract the parameter values of complex RTSs as imperative information with which researchers can draw meaningful and relevant interpretations and inferences of given devices and systems.