No abstract

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.

We observe rich phenomena of two-level random telegraph noise (RTN) from a commercial bulk 28-nm p-MOSFET (PMOS) near threshold at 14 K, where a Coulomb blockade (CB) hump arises from a quantum dot (QD) formed in the channel. Minimum RTN is observed at the CB hump where the high-current RTN level dramatically switches to the low-current level. The gate-voltage dependence of the RTN amplitude and power spectral density match well with the transconductance from the DC transfer curve in the CB hump region. Our work unequivocally captures these QD transport signatures in both current and noise, revealing quantum confinement effects in commercial short-channel PMOS even at 14 K, over 100 times higher than the typical dilution refrigerator temperatures of QD experiments (<100 mK). We envision that our reported RTN characteristics rooted from the QD and a defect trap would be more prominent for smaller technology nodes, where the quantum effect should be carefully examined in cryogenic CMOS circuit designs.

Langmuir films of pure, solution-exfoliated hexagonal boron nitride, transferable to arbitrary substrates, are demonstrated as promising dielectric layers suitable for transparent and flexible optoelectronics.

Carbon nanotubes (CNTs) are proving to be versatile nanomaterials that exhibit superior and attractive electrical, optical, chemical, physical, and mechanical properties. Different kinds of CNTs exist, and their associated properties have been actively explored and widely exploited from fundamental studies to practical applications. Obtaining high‐quality CNTs in large volumes is desirable, especially for scalable electronic, photonic, chemical, and mechanical systems. At present, abundant but random CNTs are synthesized by various growth methods including arc discharge, chemical vapor deposition, and molecular beam epitaxy. An economical way to secure pristine CNTs is to disperse the raw soot of CNTs in solutions, from which purified CNTs are collected via sorting methods. Individual CNTs are generally hydrophobic, not readily soluble, requiring an agent, known as a surfactant to facilitate effective dispersions. Furthermore, the combination of surfactants, polymers, DNA, and other additives can enhance the purity of specific types of CNTs in confidence dispersions. With highly‐pure CNTs, designated functional devices are built to demonstrate improved performance. This review surveys and highlights the essential roles and significant impacts of surfactants in dispersing and sorting CNTs.

Microcavity exciton-polaritons are attractive quantum quasi-particles resulting from strong light-matter coupling in a quantum-well-cavity structure. They have become one of the most stimulating solid-state material platforms to explore beautiful collective quantum phenomena originating from macroscopic coherence in condensation and superfluidity, Berezinskii-Kosterlitz-Thouless transition, and various topological excitations in the form of solitons, vortices, and skyrmions. They can also provide opportunities for the development of pioneering photonic devices by exploiting bistability and parametric scatterings due to strong nonlinearity that possess remarkable performance advantages of power-efficient operation, ultrafast response time, and scalable planar geometries. This story becomes profound and fascinating when the spins of excitons are taken into account, that can be directly accessed through light polarization states. The purpose of this review is to give central principles of microcavity exciton-polariton spins and their anisotropic interactions, which can couple with the effective magnetic fields from mode-splitting of microcavity photons and spin-dependent relaxation processes of quantum-well excitons. Furthermore, notable theoretical and experimental research activities are summarized to reveal extraordinary quantum phenomena of spin-resolved topological states and exotic spin textures and to devise novel spin-based photonic devices based on microcavity exciton-polaritons.

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. Reliable RTS analyses are crucial prerequisite to identify underlying mechanisms related to performance sensitivity. When numerous levels partake, complex patterns of multilevel RTSs occur, making their quantitative analysis exponentially difficult, hereby systematic approaches are found elusive. Here, we present a three-step analysis protocol via progressive knowledge-transfer, where the outputs of early step are passed onto a subsequent step. Especially, to quantify complex RTSs, we build three deep neural network architectures that can process temporal data well and demonstrate the model accuracy extensively with a large dataset of different RTS types affected by controlling background noise size. Our protocol offers structured schemes to quantify complex RTSs from which meaningful interpretation and inference can ensue.

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.