Bismuth halide thin films for resistive random access memory device

Resistive random-access memory (RRAM) is a kind of highly promising non-volatile memory technology. Recently, halide perovskites have aroused attention worldwide because of their outstanding resistive switching performance and ease of fabrication. The advantages of the halide perovskite devices include high ON/OFF ratio and low operation voltage, enabling excellent device performance with low power consumption. Currently, the most widely studied halide perovskites contain lead, which is a toxic element that may incur serious environmental problems and significant harm to human health. In order to address these issues, there is a pressing need to develop lead-free halide perovskites and their derivatives possessing comparable functional properties to their lead-based counterparts. Bismuth-based halide perovskites have emerged as a promising lead-free alternative for applications in RRAM. A great advantage of bismuth-based halide perovskites lies in their high solubility for various elements, thus offering the possibility of the formation of modified compositions to tailor the resistive switching behaviours including ON/OFF ratio, endurance and retention. Cs3Bi2I9 and MA3Bi2I9 (MA = methylammonium) are two common lead-free perovskite halides that have been widely studied for RRAM. However, doping in Cs3Bi2I9 and MA3Bi2I9 is normally conducted on a single chemical site (either A-site or X site) and the impact of co-doping on their resistive switching properties remains less explored. In this project, thin films of several co-doped compositions namely MA2CsBi2BrxI9-x (x=2, 3, 4, 5, 6, 7, 8) were prepared to investigate the double doping (Cs on A-site, Br on X-site) effects on their structural, morphological and electrical properties. In addition, the effect of different top electrodes (Ag and Au) on the electrical performance of the MA2CsBi2BrxI9-x thin films was also studied. It was found that more uniform and denser thin films could be obtained with an increase in Br content. Among the several compositions under investigation, MA2CsBi2Br8I-based thin film with Au top electrodes exhibited typical resistive switching behaviour and an interface-type conduction mechanism. When the perovskites layer was covered by Ag top electrodes, the distinct resistive switching behaviour could be observed with the increase of I content, which could be attributed to the redox reaction of Ag electrodes and iodide ions at the interface between electrodes and the active layer. Compared to other compositions, MA2CsBi2Br2I7-based thin film with Ag electrodes exhibited an outstanding ON/OFF ratio of around 105. Since the MA2CsBi2Br8I perovskite had good endurance and full-coverage surface, the MA2CsBi2Br8I perovskite was employed for further study. Au/MA2CsBi2Br8I/ITO devices with different thicknesses (290 nm, 307 nm, 341 nm and 435 nm) showed stable bipolar resistive switching behaviours. With the increasing thickness, the SET electric field remains around 6.5 V/μm, which is nearly independent of film thickness. When the thickness of the MA2CsBi2Br8I perovskite layer increased from 136 nm to 307 nm, the device demonstrated better stability over 100 cycles and a higher ON/OFF ratio (~10) at a low reading voltage of 0.27 V

Diffusion Mechanism in Residual Neural Network: Theory and Applications

Diffusion, a fundamental internal mechanism emerging in many physical processes, describes the interaction among different objects. In many learning tasks with limited training samples, the diffusion connects the labeled and unlabeled data points and is a critical component for achieving high classification accuracy. Many existing deep learning approaches directly impose the fusion loss when training neural networks. In this work, inspired by the convection-diffusion ordinary differential equations (ODEs), we propose a novel diffusion residual network (Diff-ResNet), internally introduces diffusion into the architectures of neural networks. Under the structured data assumption, it is proved that the proposed diffusion block can increase the distance-diameter ratio that improves the separability of inter-class points and reduces the distance among local intra-class points. Moreover, this property can be easily adopted by the residual networks for constructing the separable hyperplanes. Extensive experiments of synthetic binary classification, semi-supervised graph node classification and few-shot image classification in various datasets validate the effectiveness of the proposed method

A Gate-All-Around Single-Channel In2O3 Nanoribbon FET with Near 20 mA/{\mu}m Drain Current

In this work, we demonstrate atomic-layer-deposited (ALD) single-channel indium oxide (In2O3) gate-all-around (GAA) nanoribbon FETs in a back-end-of-line (BEOL) compatible process. A maximum on-state current (ION) of 19.3 mA/{\mu}m (near 20 mA/{\mu}m) is achieved in an In2O3 GAA nanoribbon FET with a channel thickness (TIO) of 3.1 nm, channel length (Lch) of 40 nm, channel width (Wch) of 30 nm and dielectric HfO2 of 5 nm. The record high drain current obtained from an In2O3 FET is about one order of magnitude higher than any conventional single-channel semiconductor FETs. This extraordinary drain current and its related on-state performance demonstrate ALD In2O3 is a promising oxide semiconductor channel with great opportunities in BEOL compatible monolithic 3D integration