GaAs Nanowires on Si Nanopillars : Towards Large Scale, Phase-engineered Arrays.

Large-scale patterning for vapor-liquid-solid growth of III-V nanowires is a challenge given the required feature size for patterning (45 to 60nm holes). In fact, arrays are traditionally manufactured using electron-beam...

FG-Droid: Grouping Based Feature Size Reduction for Android Malware Detection through Machine Learning

The number of applications prepared for use on mobile devices has increased rapidly with the widespread use of the Android OS. This has resulted in the undesired installation of Android apks that violate user privacy or malicious. The increasing similarity between Android malware and benign applications makes it difficult to distinguish them from each other and causes a situation of concern for users. In this study, FG-Droid, a machine-learning based classifier with an efficient working system, using the method of grouping the features obtained by static analysis, was proposed. It was created as a result of experiments with Machine learning (ML), DNN, RNN, LSTM and GRU based models using Drebin, Genome and Arslan datasets. Experimental results reveal that FG-Droid has achieved 97.7% AUC score with a vector includes only 11 static features, and ExtraTree algorithm. FG-Droid analyze the applications with using the least number of features compare to previous studies, and required the least processing time for training and prediction. As a result, it has been shown that Android malware can be detected in high accuracy rate with an effective feature set and there is no need to use a large number of features extracted with different techniques (static, dynamic or hybrid).

Nanoscale electrochemical 3D deposition of cobalt with nanosecond voltage pulses in an STM

To explore a minimal feature size of <100 nm with electrochemical additive manufacturing, we use a strategy originally applied to microscale electrochemical machining for the nanoscale deposition of Co on Au. The concept’s essence is the localization of electrochemical reactions below a probe during polarization with ns-long voltage pulses. As shown, a confinement that exceeds that predicted by a simple model based on the time constant for one-dimensional double layer charging enables a feature size of <50 nm for 2D patterning. We further indirectly verify the potential for out-of-plane deposition by tracking growth curves of high-aspect-ratio deposits. Importantly, we report a lack of anodic stability of Au tips used for patterning. As an inert probe is the prerequisite for controlled structuring, we experimentally verify an increased resistance of Pt probes against degradation. Consequently, the developed setup and processes show a path towards reproducible direct 2D and 3D patterning of metals at the nanoscale.

Aerial and Optical Images-Based Plant Species Segmentation Using Enhancing Nested Downsampling Features

Plant species, structural combination, and spatial distribution in different regions should be adapted to local conditions, and the reasonable arrangement can bring the best ecological effect. Therefore, it is essential to understand the classification and distribution of plant species. This paper proposed an end-to-end network with Enhancing Nested Downsampling features (END-Net) to solve complex and challenging plant species segmentation tasks. There are two meaningful operations in the proposed network: (1) A compact and complete encoder–decoder structure nests in the down-sampling process; it makes each downsampling block obtain the equal feature size of input and output to get more in-depth plant species information. (2) The downsampling process of the encoder–decoder framework adopts a novel pixel-based enhance module. The enhanced module adaptively enhances each pixel’s features with the designed learnable variable map, which is as large as the corresponding feature map and has n×n variables; it can capture and enhance each pixel’s information flexibly effectively. In the experiments, our END-Net compared with eleven state-of-the-art semantic segmentation architectures on the self-collected dataset, it has the best PA (Pixel Accuracy) score and FWloU (Frequency Weighted Intersection over Union) accuracy and achieves 84.52% and 74.96%, respectively. END-Net is a lightweight model with excellent performance; it is practical in complex vegetation distribution with aerial and optical images. END-Net has the following merits: (1) The proposed enhancing module utilizes the learnable variable map to enhance features of each pixel adaptively. (2) We nest a tiny encoder–decoder module into the downsampling block to obtain the in-depth plant species features with the same scale in- and out-features. (3) We embed the enhancing module into the nested model to enhance and extract distinct plant species features. (4) We construct a specific plant dataset that collects the optical images-based plant picture captured by drone with sixteen species.

Versatile carbon-loaded shellac ink for disposable printed electronics

Emerging technologies such as smart packaging are shifting the requirements on electronic components, notably regarding service life, which counts in days instead of years. As a result, standard materials are often not adapted due to economic, environmental or manufacturing considerations. For instance, the use of metal conductive tracks in disposable electronics is a waste of valuable resources and their accumulation in landfills is an environmental concern. In this work, we report a conductive ink made of carbon particles dispersed in a solution of shellac. This natural and water-insoluble resin works as a binder, favourably replacing petroleum-derived polymers. The carbon particles provide electrical conductivity and act as a rheology modifier, creating a printable shear-thinning gel. The ink’s conductivity and sheet resistance are 1000 S m−1 and 15 Ω sq−1, respectively, and remain stable towards moisture. We show that the ink is compatible with several industry-relevant patterning methods such as screen-printing and robocasting, and demonstrate a minimum feature size of 200 μm. As a proof-of-concept, a resistor and a capacitor are printed and used as deformation and proximity sensors, respectively.

A Strategy to Mitigate Single Event Upset in 14 nm CMOS Bulk FinFET Technology

3D TCAD simulations demonstrated that reducing the distance between the well boundary and NMOS or PMOS can mitigate the cross section of Single Event Upset (SEU) in 14 nm CMOS bulk FinFET technology. The competition of charge collection between well boundary and sensitive nodes, the enhanced restore currents and the change of bipolar effect are responsible for the decrease of SEU cross section. Different from Dual-interlock cells (DICE) design, under the presence of enough taps to ensure the rapid recovery of well potential, this approach is more effective under heavy ion irradiation of higher LET. Besides, the feasibility of this method and its effectiveness with feature size scaling down are discussed.

Super-Resolution Imaging with Patchy Microspheres

The diffraction limit is a fundamental barrier in optical microscopy, which restricts the smallest resolvable feature size of a microscopic system. Microsphere-based microscopy has proven to be a promising tool for challenging the diffraction limit. Nevertheless, the microspheres have a low imaging contrast in air, which hinders the application of this technique. In this work, we demonstrate that this challenge can be effectively overcome by using partially Ag-plated microspheres. The deposited Ag film acts as an aperture stop that blocks a portion of the incident beam, forming a photonic hook and an oblique near-field illumination. Such a photonic hook significantly enhanced the imaging contrast of the system, as experimentally verified by imaging the Blu-ray disc surface and colloidal particle arrays.

Metamaterial-Engineered Silicon Beam Splitter Fabricated with Deep UV Immersion Lithography

Subwavelength grating (SWG) metamaterials have garnered a great interest for their singular capability to shape the material properties and the propagation of light, allowing the realization of devices with unprecedented performance. However, practical SWG implementations are limited by fabrication constraints, such as minimum feature size, that restrict the available design space or compromise compatibility with high-volume fabrication technologies. Indeed, most successful SWG realizations so far relied on electron-beam lithographic techniques, compromising the scalability of the approach. Here, we report the experimental demonstration of an SWG metamaterial engineered beam splitter fabricated with deep-ultraviolet immersion lithography in a 300-mm silicon-on-insulator technology. The metamaterial beam splitter exhibits high performance over a measured bandwidth exceeding 186 nm centered at 1550 nm. These results open a new route for the development of scalable silicon photonic circuits exploiting flexible metamaterial engineering.

The pursuit of further miniaturization of screen printed micro paper-based analytical devices utilizing controlled penetration towards optimized channel patterning

One of the main objectives of microfluidic paper-based analytical devices is to present solutions particularly, for applications in low-resource settings. Therefore, screen-printing appears to be an attractive fabrication technique in the field, due to its overall simplicity, affordability, and high-scalability potential. Conversely, the minimum feature size attained using screen-printing is still rather low, especially compared to other fabrication methods, mainly attributed to the over-penetration of hydrophobic agents, underneath defined patterns on masks, into the fiber matrix of paper substrates. In this work, we propose the use of the over-penetration to our advantage, whereby an appropriate combination of hydrophobic agent temperature and substrate thickness, allows for the proper control of channel patterning, rendering considerably higher resolutions than prior arts. The implementation of Xuan paper and nail oil as novel substrate and hydrophobic agent, respectively, is proposed in this work. Under optimum conditions of temperature and substrate thickness, the resolution of the screen-printing method was pushed up to 97.83 ± 16.34 μm of channel width with acceptable repeatability. It was also found that a trade-off exists between achieving considerably high channel resolutions and maintaining high levels of repeatability of the process. Lastly, miniaturized microfluidic channels were successfully patterned on pH strips for colorimetric pH measurement, demonstrating its advantage on negligible sample-volume consumption in nano-liter range during chemical measurement and minimal interference on manipulation of precious samples, which for the first time, is realized on screen-printed microfluidic paper-based analytical devices.

Experimental Research of Static Characteristics of Microfluidic Trigger

Experimental research of the main static characteristics of the microfluidic trigger of the 100-micrometer feature size is carried out. In order to research the trigger, the experimental (physical) model has been especially made. As the main result of the research, the transfer and flow-rate characteristics of the trigger are obtained in relation to the two connection schemes: 1) the relay scheme with the two-input control, 2) the comparator scheme with the control at a first input and with a reference setpoint at a second input.