Abstract:Flash memory is the most widely used non-volatile memory device nowadays. In order to keep up with the demand for increased memory capacities, flash memory has been continuously scaled to smaller and smaller dimensions. The main benefits of down-scaling cell size and increasing integration are that they enable lower manufacturing cost as well as higher performance. Charge trapping memory is regarded as one of the most promising flash memory technologies as further down-scaling continues. In addition, more and more exploration is investigated with high-k dielectrics implemented in the charge trapping memory. The paper reviews the advanced research status concerning charge trapping memory with high-k dielectrics for the performance improvement. Application of high-k dielectric as charge trapping layer, blocking layer, and tunneling layer is comprehensively discussed accordingly.

Abstract:Resistive random access memory (RRAM) devices are receiving increasing extensive attention due to their enhanced properties such as fast operation speed, simple device structure, low power consumption, good scalability potential and so on, and are currently considered to be one of the next-generation alternatives to traditional memory. In this review, an overview of RRAM devices is demonstrated in terms of thin film materials investigation on electrode and function layer, switching mechanisms and artificial intelligence applications. Compared with the well-developed application of inorganic thin film materials (oxides, solid electrolyte and two-dimensional (2D) materials) in RRAM devices, organic thin film materials (biological and polymer materials) application is considered to be the candidate with significant potential. The performance of RRAM devices is closely related to the investigation of switching mechanisms in this review, including thermal-chemical mechanism (TCM), valance change mechanism (VCM) and electrochemical metallization (ECM). Finally, the bionic synaptic application of RRAM devices is under intensive consideration, its main characteristics such as potentiation/depression response, short-/long-term plasticity (STP/LTP), transition from short-term memory to long-term memory (STM to LTM) and spike-time-dependent plasticity (STDP) reveal the great potential of RRAM devices in the field of neuromorphic application.

Abstract:In recent years, deep learning models have achieved remarkable successes in various applications, such as pattern recognition, computer vision, and signal processing. However, high-performance deep architectures are often accompanied by a large storage space and long computational time, which make it difficult to fully exploit many deep neural networks (DNNs), especially in scenarios in which computing resources are limited. In this paper, to tackle this problem, we introduce a method for compressing the structure and parameters of DNNs based on neuron agglomerative clustering (NAC). Specifically, we utilize the agglomerative clustering algorithm to find similar neurons, while these similar neurons and the connections linked to them are then agglomerated together. Using NAC, the number of parameters and the storage space of DNNs are greatly reduced, without the support of an extra library or hardware. Extensive experiments demonstrate that NAC is very effective for the neuron agglomeration of both the fully connected and convolutional layers, which are common building blocks of DNNs, delivering similar or even higher network accuracy. Specifically, on the benchmark CIFAR-10 and CIFAR-100 datasets, using NAC to compress the parameters of the original VGGNet by 92.96%% and 81.10%%, respectively, the compact network obtained still outperforms the original networks.

Abstract:Data searching and retrieval is one of the fundamental functionalities in many Web of Things applications, which need to collect, process and analyze huge amounts of sensor stream data. The problem in fact has been well studied for data generated by sensors that are installed at fixed locations; however, challenges emerge along with the popularity of opportunistic sensing applications in which mobile sensors keep reporting observation and measurement data at variable intervals and changing geographical locations. To address these challenges, we develop the Geohash-Grid Tree, a spatial indexing technique specially designed for searching data integrated from heterogeneous sources in a mobile sensing environment. Results of the experiments on a real-world dataset collected from the SmartSantander smart city testbed show that the index structure allows efficient search based on spatial distance, range and time windows in a large time series database.

Abstract:In this research, the hafnium titanate oxide thin films, TixHf1-xO2, with titanium contents of x = 0, 0.25, 0.9, and 1 were deposited on germanium substrates by atomic layer deposition (ALD) at 300 degrees C. The approximate deposition rates of 0.2 angstrom and 0.17 angstrom per cycle were obtained for titanium oxide and hafnium oxide, respectively. X-ray Photoelectron Spectroscopy (XPS) indicates the formation of GeOx and germanate at the interface. X-ray diffraction (XRD) indicates that all the thin films remain amorphous for this deposition condition. The surface roughness was analyzed using an atomic force microscope (AFM) for each sample. The electrical characterization shows very low hysteresis between ramp up and ramp down of the Capacitance-Voltage (CV) and the curves are indicative of low trap densities. A relatively large leakage current is observed and the lowest leakage current among the four samples is about 1 mA/cm(2) at a bias of 0.5 V for a Ti0.9Hf0.1O2 sample. The large leakage current is partially attributed to the deterioration of the interface between Ge and TixHf1-xO2 caused by the oxidation source from HfO2. Consideration of the energy band diagrams for the different materials systems also provides a possible explanation for the observed leakage current behavior.

Abstract:The Web of Things aims to make physical world objects and their data accessible through standard Web technologies to enable intelligent applications and sophisticated data analytics. Due to the amount and heterogeneity of the data, it is challenging to perform data analysis directly; especially when the data is captured from a large number of distributed sources. However, the size and scope of the data can be reduced and narrowed down with search techniques, so that only the most relevant and useful data items are selected according to the application requirements. Search is fundamental to the Web of Things while challenging by nature in this context, e.g., mobility of the objects, opportunistic presence and sensing, continuous data streams with changing spatial and temporal properties, efficient indexing for historical and real time data. The research community has developed numerous techniques and methods to tackle these problems as reported by a large body of literature in the last few years. A comprehensive investigation of the current and past studies is necessary to gain a clear view of the research landscape and to identify promising future directions. This survey reviews the state-of-the-art search methods for the Web of Things, which are classified according to three different viewpoints: basic principles, data/knowledge representation, and contents being searched. Experiences and lessons learned from the existing work and some EU research projects related to Web of Things are discussed, and an outlook to the future research is presented.

Abstract:A powerful characterization technique, pulse capacitance-voltage (CV) technique, was used to investigate oxide traps before and after annealing for lanthanide zirconium oxide thin films deposited on n-type Si (111) substrates at 300 degrees C by liquid injection Atomic Layer Deposition (ALD). The results indicated that: (1) more traps were observed compared to the conventional capacitance-voltage characterization method in LaZrOx; (2) the time-dependent trapping/de-trapping was influenced by the edge time, width and peak-to-peak voltage of a gate voltage pulse. Post deposition annealing was performed at 700 degrees C, 800 degrees C and 900 degrees C in N-2 ambient for 15 s to the samples with 200 ALD cycles. The effect of the high temperature annealing on oxide traps and leakage current were subsequently explored. It showed that more traps were generated after annealing with the trap density increasing from 1.41 x 10(12) cm(-2) for as-deposited sample to 4.55 x 10(12) cm(-2) for the 800 degrees C annealed one. In addition, the leakage current density increase from about 10(-6) A/cm(2) at V-g = +0.5 V for the as-deposited sample to 10(-3) A/cm(2) at V-g = +0.5 V for the 900 degrees C annealed one.

Abstract:Uncertainty is intrinsic in most of the complex systems, especially when the systems have to interact with the physical environment; therefore, handling uncertainty is critical in the Internet of Things (IoT). In this paper, we propose a semantic-based approach to build the belief network in IoT systems to handle the uncertainties. Semantics is the functionality description of any system component. Semantic Match mechanisms can construct the appropriate structures to compare the consistency between different sources of data based on the same functionality. In the approach, we define the belief property of every system component and develop the related algorithms to update the belief value. Furthermore, the related mechanisms and algorithms for data fusion and fault detection based on the belief property are described to explain how the approach works in the IoT systems. Several simulation experiments are used to evaluate the proposed approach, and the results indicate that the approach can work as expected. More accurate data are fused from the inaccurate devices and the fault in one node is automatically detected.

Abstract:Oxide materials with large dielectric constants (so-called high-k dielectrics) have attracted much attention due to their potential use as gate dielectrics in Metal Oxide Semiconductor Field Effect Transistors (MOSFETs). A novel characterization (pulse capacitance-voltage) method was proposed in detail. The pulse capacitance-voltage technique was employed to characterize oxide traps of high-k dielectrics based on the Metal Oxide Semiconductor (MOS) capacitor structure. The variation of flat-band voltages of the MOS structure was observed and discussed accordingly. Some interesting trapping/detrapping results related to the lanthanide aluminum oxide traps were identified for possible application in Flash memory technology. After understanding the trapping/detrapping mechanism of the high-k oxides, a solid foundation was prepared for further exploration into charge-trapping non-volatile memory in the future.

Abstract:Extraction of relevant lip features is of continuing interest in the visual speech domain. Using end-to-end feature extraction can produce good results, but at the cost of the results being difficult for humans to comprehend and relate to. We present a new, lightweight feature extraction approach, motivated by human-centric glimpse-based psychological research into facial barcodes, and demonstrate that these simple, easy to extract 3D geometric features (produced using Gabor-based image patches), can successfully be used for speech recognition with LSTM-based machine learning. This approach can successfully extract low dimensionality lip parameters with a minimum of processing. One key difference between using these Gabor-based features and using other features such as traditional DCT, or the current fashion for CNN features is that these are human-centric features that can be visualised and analysed by humans. This means that it is easier to explain and visualise the results. They can also be used for reliable speech recognition, as demonstrated using the Grid corpus. Results for overlapping speakers using our lightweight system gave a recognition rate of over 82%%, which compares well to less explainable features in the literature.

Abstract:Virtual reality (VR) has advanced rapidly and is used for many entertainment and business purposes. The need for secure, transparent and non-intrusive identification mechanisms is important to facilitate users' safe participation and secure experience. People are kinesiologically unique, having individual behavioral and movement characteristics, which can be leveraged and used in security sensitive VR applications to compensate for users' inability to detect potential observational attackers in the physical world. Additionally, such method of identification using a user's kinesiological data is valuable in common scenarios where multiple users simultaneously participate in a VR environment. In this paper, we present a user study (n = 15) where our participants performed a series of controlled tasks that require physical movements (such as grabbing, rotating and dropping) that could be decomposed into unique kinesiological patterns while we monitored and captured their hand, head and eye gaze data within the VR environment. We present an analysis of the data and show that these data can be used as a biometric discriminant of high confidence using machine learning classification methods such as kNN or SVM, thereby adding a layer of security in terms of identification or dynamically adapting the VR environment to the users' preferences. We also performed a whitebox penetration testing with 12 attackers, some of whom were physically similar to the participants. We could obtain an average identification confidence value of 0.98 from the actual participants' test data after the initial study and also a trained model classification accuracy of 98.6%%. Penetration testing indicated all attackers resulted in confidence values of less than 50%% (<50%%), although physically similar attackers had higher confidence values. These findings can help the design and development of secure VR systems.

Abstract:The 4 at. %% zirconium-doped zinc oxide (ZnO: Zr) films grown by atomic layer deposition (ALD) were annealed at various temperatures ranging from 350 to 950 degrees C. The structural, electrical, and optical properties of rapid thermal annealing (RTA) treated ZnO: Zr films have been evaluated to find out the stability limit. It was found that the grain size increased at 350 degrees C and decreased between 350 and 850 degrees C, while creeping up again at 850 degrees C. UV-vis characterization shows that the optical band gap shifts towards larger wavelengths. The Hall measurement shows that the resistivity almost keeps constant at low annealing temperatures, and increases rapidly after treatment at 750 degrees C due to the effect of both the carrier concentration and the Hall mobility. The best annealing temperature is found in the range of 350-550 degrees C. The ZnO: Zr film-coated glass substrates show good optical and electrical performance up to 550 degrees C during superstrate thin film solar cell deposition.

Abstract:Current surgical treatments for meniscal tears suffer from subsequent degeneration of knee joints, limited donor organs and inconsistent post-treatment results. Three clinical scaffolds (Menaflex CMI, Actifit(R) scaffold and NUsurface(R) Meniscus Implant) are available on the market, but additional data are needed to properly evaluate their safety and effectiveness. Thus, many scaffold-based research activities have been done to develop new materials, structures and fabrication technologies to mimic native meniscus for cell attachment and subsequent tissue development, and restore functionalities of injured meniscus for long-term effects. This study begins with a synopsis of relevant structural features of meniscus and goes on to describe the critical considerations. Promising advances made in the field of meniscal scaffolding technology, in terms of biocompatible materials, fabrication methods, structure design and their impact on mechanical and biological properties are discussed in detail. Among all the scaffolding technologies, additive manufacturing (AM) is very promising because of its ability to precisely control fiber diameter, orientation, and pore network micro-architecture to mimic the native meniscus microenvironment.

Abstract:In capacitance-voltage (C-V) measurements, frequency dispersion in high-k dielectrics is often observed. The frequency dependence of the dielectric constant (k-value), that is the intrinsic frequency dispersion, could not be assessed before suppressing the effects of extrinsic frequency dispersion, such as the effects of the lossy interfacial layer (between the high-k thin film and silicon substrate) and the parasitic effects. The effect of the lossy interfacial layer on frequency dispersion was investigated and modeled based on a dual frequency technique. The significance of parasitic effects (including series resistance and the back metal contact of the metal-oxide-semiconductor (MOS) capacitor) on frequency dispersion was also studied. The effect of surface roughness on frequency dispersion is also discussed. After taking extrinsic frequency dispersion into account, the relaxation behavior can be modeled using the Curie-von Schweidler (CS) law, the Kohlrausch-Williams-Watts (KWW) relationship and the Havriliak-Negami (HN) relationship. Dielectric relaxation mechanisms are also discussed.

Abstract:A facile and environmentally friendly fabrication is proposed to prepare nitrogen-doped hierarchical porous activated carbon via normal-pressure popping, one-pot activation and nitrogen-doping process. The method adopts paddy as carbon precursor, KHCO3 and dicyandiamide as the safe activating agent and nitrogen dopant. The as-prepared activated carbon presents a large specific surface area of 3025 m(2)center dot g(-1) resulting from the synergistic effect of KHCO3 and dicyandiamide. As an electrode material, it shows a maximum specific capacitance of 417 F center dot g(-1) at a current density of 1 A center dot g(-1) and very good rate performance. Furthermore, the assembled symmetric supercapacitor presents a large specific capacitance of 314.6 F center dot g(-1) and a high energy density of 15.7 Wh center dot Kg(-1) at 1 A center dot g(-1), maintaining 14.4 Wh center dot Kg(-1) even at 20 A center dot g(-1) with the energy density retention of 91.7%%. This research demonstrates that nitrogen-doped hierarchical porous activated carbon derived from paddy has a significant potential for developing a high-performance renewable supercapacitor and provides a new route for economical and large-scale production in supercapacitor application.

Abstract:Thin-film transistors (TFTs) made of metal oxide semiconductors are now increasingly used in flat-panel displays. Metal oxides are mainly fabricated via vacuum-based technologies, but solution approaches are of great interest due to the advantages of low-cost and high-throughput manufacturing. Unfortunately, solution-processed oxide TFTs suffer from relatively poor electrical performance, hindering further development. Recent studies suggest that this issue could be solved by introducing a novel heterojunction strategy. This article reviews the recent advances in solution-processed heterojunction oxide TFTs, with a specific focus on the latest developments over the past five years. Two of the most prominent advantages of heterostructure oxide TFTs are discussed, namely electrical-property modulation and mobility enhancement by forming 2D electron gas. It is expected that this review will manifest the strong potential of solution-based heterojunction oxide TFTs towards high performance and large-scale electronics.