A WO/MoO hybrid oxide based SERS FET and investigation on its tunable SERS performance.

Active control of the surface-enhanced Raman scattering (SERS) enhancement shows great potential for realizing smart detection of different molecules. However, conventional methods usually involve time-consuming structural design or a sophisticated fabrication process. Herein, we reported an electrically tunable field effect transistor (FET) comprising a WO/MoO hybrid as the SERS active layer.

Emerging memristive artificial neuron and synapse devices for the neuromorphic electronics era.

Growth of data eases the way to access the world but requires increasing amounts of energy to store and process. Neuromorphic electronics has emerged in the last decade, inspired by biological neurons and synapses, with in-memory computing ability, extenuating the 'von Neumann bottleneck' between the memory and processor and offering a promising solution to reduce the efforts both in data storage and processing, thanks to their multi-bit non-volatility, biology-emulated characteristics, and silicon compatibility. This work reviews the recent advances in emerging memristive devices for artificial neuron and synapse applications, including memory and data-processing ability: the physics and characteristics are discussed first, , valence changing, electrochemical metallization, phase changing, interfaced-controlling, charge-trapping, ferroelectric tunnelling, and spin-transfer torquing.

Insights into the Semiconductor SERS Activity: The Impact of the Defect-Induced Energy Band Offset and Electron Lifetime Change.

The significant boost in surface-enhanced Raman scattering (SERS) by the chemical enhancement of semiconducting oxides is a pivotal finding. It offers a prospective path toward high uniformity and low-cost SERS substrates. However, a detailed understanding of factors that influence the charge transfer process is still insufficient.

Conductive Bridge Random Access Memory (CBRAM): Challenges and Opportunities for Memory and Neuromorphic Computing Applications.

Due to a rapid increase in the amount of data, there is a huge demand for the development of new memory technologies as well as emerging computing systems for high-density memory storage and efficient computing. As the conventional transistor-based storage devices and computing systems are approaching their scaling and technical limits, extensive research on emerging technologies is becoming more and more important. Among other emerging technologies, CBRAM offers excellent opportunities for future memory and neuromorphic computing applications.

A novel fabrication technique for high-aspect-ratio nanopillar arrays for SERS application.

A novel technique is demonstrated for the fabrication of silicon nanopillar arrays with high aspect ratios. Our technique leverages on an "antenna effect" present on a chromium (Cr) hard mask during ion-coupled plasma (ICP) etching. Randomly distributed sharp tips around the Cr edge act as antennas that attract etchant ions, which in turn enhance the etching of the Cr edge.

Implementation of Simple but Powerful Trilayer Oxide-Based Artificial Synapses with a Tailored Bio-Synapse-Like Structure.

The ultimate aim of artificial synaptic devices is to mimic the features of biological synapses as closely as possible, in particular, its ability of self-adjusting the synaptic weight responding to the external stimulus. In this work, memristors, based on trilayer oxides with a stack structure of TiN/TiON/HfO/HfO/TiN, are designed to function as the artificial synapses where intrinsically designed oxygen-deficient HfO layer, less oxygen-deficient HfO layer, and TiON layer, imitating the corresponding biological functionality of the pre-synapse, synaptic cleft, and post-synapse, respectively, resemble the features of bio-synapses most closely. Thus, diverse bio-synaptic functions and plasticity, including long-term potentiation and depression, spike-rate-dependent plasticity, spike-timing-dependent plasticity, and metaplasticity, are fulfilled in these devices.

On-Demand Visible-Light Sensing with Optical Memory Capabilities Based on an Electrical-Breakdown-Triggered Negative Photoconductivity Effect in the Ubiquitous Transparent Hafnia.

A transparent visible-light sensor may sound like an oxymoron. Indeed, this scenario is often deemed challenging in conventional photosensitive semiconducting materials due to the complementary relationship between absorbance (which determines photosensitivity) and transmittance (which determines transparency). Past studies have relied on photoinduced ionization of vacancy defect states within a wide-band-gap oxide to modulate the flow of current or charge storage in specific device structures such as nanorods, hetero oxide junctions, or thin-film transistors.

Realization of Self-Compliance Resistive Switching Memory via Tailoring Interfacial Oxygen.

Self-compliance set switching (SCSS) offers the promise of a selector-less resistive random access memory (RRAM) implementation on flexible substrates, with great application in integrated flexible electronics. SCSS has been realized in RRAM devices with a series oxide layer incorporated into the memory stack. The series oxide acts as an in-built resistor, limiting the increase of the current during set transition.

Electrical Tuning of the SERS Enhancement by Precise Defect Density Control.

Surface-enhanced Raman scattering (SERS) has been widely established as a powerful analytical technique in molecular fingerprint recognition. Although conventional noble metal-based SERS substrates show admirable enhancement of the Raman signals, challenges on reproducibility, biocompatibility, and costs limit their implementations as the preferred analysis platforms. Recently, researches on SERS substrates have found that some innovatively prepared metal oxides/chalcogenides could produce noble metal comparable SERS enhancement, which profoundly expanded the material selection.

Nanoscale Conductive Filament with Alternating Rectification as an Artificial Synapse Building Block.

A popular approach for resistive memory (RRAM)-based hardware implementation of neural networks utilizes one (or two) device that functions as an analog synapse in a crossbar structure of perpendicular pre- and postsynaptic neurons. An ideal fully automated, large-scale artificial neural network, which matches a biologic counterpart (in terms of density and energy consumption), thus requires nanosized, extremely low power devices with a wide dynamic range and multilevel functionality. Unfortunately the trade-off between these traits proves to be a serious obstacle in the realization of brain-inspired computing platforms yet to be overcome.