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In this paper, we investigate the existence problem of periodic orbits for a planar Liénard system, whose solution mappings are interrupted by abrupt changes of state. We first present the geometrical properties of solutions for the planar Liénard system with state impulses, then by using Bendixson theorem of impulsive differential equations and successor function method, several new criteria on the closed orbits and discontinuous periodic orbits are established in the impulsive Liénard system. [ABSTRACT FROM AUTHOR]

Memristors and memristive systems have been extensively studied for data storage and computing applications such as neuromorphic systems. To act as synapses in neuromorphic systems, the memristor needs to exhibit analog resistive switching (RS) behavior with incremental conductance change. In this study, we show that the dynamic range of the analog RS behavior can be significantly enhanced in a tantalum-oxide-based memristor. By controlling different state variables enabled by different physical effects during the RS process, the gradual filament expansion stage can be selectively enhanced without strongly affecting the abrupt filament length growth stage. Detailed physics-based modeling further verified the observed experimental effects and revealed the roles of oxygen vacancy drift and diffusion processes, and how the diffusion process can be selectively enhanced during the filament expansion stage. These findings lead to more desirable and reliable memristor behaviors for analog computing applications. Additionally, the ability to selectively control different internal physical processes demonstrated in the current study provides guidance for continued device optimization of memristor devices in general. [ABSTRACT FROM AUTHOR]

The article reports that memristive devices exhibit an electrical resistance that can be adjusted to two or more nonvolatile levels by applying electrical stresses. Topics include considered that the core of the advanced memristive devices is a metal/insulator/metal nanocell made of phase-change, metaloxide, magnetic, or ferroelectric materials, which is placed in series with other circuit elements to enhance their performance in array configurations.

A comprehensive analysis of write operations (SET and RESET) in a resistance-change memory (resistive random access memory) crossbar array is carried out. Three types of resistive switching memory cells-nonlinear, rectifying-SET, and rectifying-RESET-are compared with each other in terms of voltage delivery, current delivery, and power consumption. Two different write schemes, V/2 and V/3, were considered, and the V/2 write scheme is preferred due to much lower power consumption. A simple numerical method was developed that simulates entire current flows and node voltages within a crossbar array and provides a quantitative tool for the accurate analysis of crossbar arrays and guidelines for developing reliable write operation. [ABSTRACT FROM AUTHOR]

Memristors have been considered as a leading candidate for a number of critical applications ranging from nonvolatile memory to non-Von Neumann computing systems. Feature extraction, which aims to transform input data from a high-dimensional space to a space with fewer dimensions, is an important technique widely used in machine learning and pattern recognition applications. Here, we experimentally demonstrate that memristor arrays can be used to perform principal component analysis, one of the most commonly used feature extraction techniques, through online, unsupervised learning. Using Sanger's rule, that is, the generalized Hebbian algorithm, the principal components were obtained as the memristor conductances in the network after training. The network was then used to analyze sensory data from a standard breast cancer screening database with high classification success rate (97.1%). [ABSTRACT FROM AUTHOR]

Vertical junctionless transistors with a gate-all-around (GAA) structure based on Ge/Si core/shell nanowires epitaxially grown and integrated on a 〈111〉 Si substrate were fabricated and analyzed. Because of efficient gate coupling in the nanowire-GAA transistor structure and the high density one-dimensional hole gas formed in the Ge nanowire core, excellent P-type transistor behaviors with Ion of 750 µA/µm were obtained at a moderate gate length of 544 nm with minimal short-channel effects. The experimental data can be quantitatively modeled by a GAA junctionless transistor model with few fitting parameters, suggesting the nanowire transistors can be fabricated reliably without introducing additional factors that can degrade device performance. Devices with different gate lengths were readily obtained by tuning the thickness of an etching mask film. Analysis of the histogram of different devices yielded a single dominate peak in device parameter distribution, indicating excellent uniformity and high confidence of single nanowire operation. Using two vertical nanowire junctionless transistors, a PMOS-logic inverter with near rail-to-rail output voltage was demonstrated, and device matching in the logic can be conveniently obtained by controlling the number of nanowires employed in different devices rather than modifying device geometry. These studies show that junctionless transistors based on vertical Ge/Si core/shell nanowires can be fabricated in a controlled fashion with excellent performance and may be used in future hybrid, high-performance circuits where bottom-up grown nanowire devices with different functionalities can be directly integrated with an existing Si platform. [ABSTRACT FROM AUTHOR]

We show temporal and frequency information can be effectively encoded in memristive devices with inherent short-term dynamics. Ag/Ag2S/Pd based memristive devices with low programming voltage (~100 mV) were fabricated and tested. At weak programming conditions, the devices exhibit inherent decay due to spontaneous diffusion of the Ag atoms. When the devices were subjected to pulse train inputs emulating different spiking patterns, the switching probability distribution function diverges from the standard Poisson distribution and evolves according to the input pattern. The experimentally observed switching probability distributions and the associated cumulative probability functions can be well-explained using a model accounting for the shortterm decay effects. Such devices offer an intriguing opportunity to directly encode neural signals for neural information storage and analysis. [ABSTRACT FROM AUTHOR]

Memristors have attracted broad interest as a promising candidate for future memory and computing applications. Particularly, it is believed that memristors can effectively implement synaptic functions and enable efficient neuromorphic systems. Most previous studies, however, focus on implementing specific synaptic learning rules by carefully engineering external programming parameters instead of focusing on emulating the internal cause that leads to the apparent learning rules. Here, it is shown that by taking advantage of the different time scales of internal oxygen vacancy ( VO) dynamics in an oxide-based memristor, diverse synaptic functions at different time scales can be implemented naturally. Mathematically, the device can be effectively modeled as a second-order memristor with a simple set of equations including multiple state variables. Not only is this approach more biorealistic and easier to implement, by focusing on the fundamental driving mechanisms it allows the development of complete theoretical and experimental frameworks for biologically inspired computing systems. [ABSTRACT FROM AUTHOR]

Memristors have been extensively studied for data storage and low-power computation applications. In this study, we show that memristors offer more than simple resistance change. Specifically, the dynamic evolutions of internal state variables allow an oxide-based memristor to exhibit Ca2+-like dynamics that natively encode timing information and regulate synaptic weights. Such a device can be modeled as a second-order memristor and allow the implementation of critical synaptic functions realistically using simple spike forms based solely on spike activity. [ABSTRACT FROM AUTHOR]

Resistive switching devices (RRAMs) have been proposed a promising candidate for future memory and neuromorphic applications. Central to the successful application of these emerging devices is the understanding of the resistance switching and failure mechanism, and identification of key physical parameters that will enable continued device optimization. In this study, we report detailed retention analysis of a TaOx based RRAM at high temperatures and the development of a microscopic oxygen diffusion model that fully explains the experimental results and can be used to guide future device developments. The device conductance in low resistance state (LRS) was constantly monitored at several elevated temperatures (above 300 °C), and an initial gradual conductivity drift followed by a sudden conductance drop were observed during retention failure. These observations were explained by a microscopic model based on oxygen vacancy diffusion, which quantitatively explains both the initial gradual conductance drift and the sudden conductance drop. Additionally, a non-monotonic conductance change, with an initial conductance increase followed by the gradual conductance decay over time, was observed experimentally and explained within the same model framework. Specifically, our analysis shows that important microscopic physical parameters such as the activation energy for oxygen vacancy migration can be directly calculated from the failure time versus temperature relationship. Results from the analytical model were further supported by detailed numerical multi-physics simulation, which confirms the filamentary nature of the conduction path in LRS and the importance of oxygen vacancy diffusion in device reliability. Finally, these high-temperature stability measurements also reveal the existence of multiple filaments in the same device. [ABSTRACT FROM AUTHOR]

Memristors have emerged as a promising candidate for critical applications such as non-volatile memory as well as non-Von Neumann computing architectures based on neuromorphic and machine learning systems. In this study, we demonstrate that memristors can be used to perform principal component analysis (PCA), an important technique for machine learning and data feature learning. The conductance changes of memristors in response to voltage pulses are studied and modeled with an internal state variable to trace the analog behavior of the device. Unsupervised, online learning is achieved in a memristor crossbar using Sanger's learning rule, a derivative of Hebb's rule, to obtain the principal components. The details of weights evolution during training is investigated over learning epochs as a function of training parameters. The effects of device non-uniformity on the PCA network performance are further analyzed. We show that the memristor-based PCA network is capable of linearly separating distinct classes from sensory data with high clarification success of 97.6% even in the presence of large device variations. [ABSTRACT FROM AUTHOR]

Background: Cytotoxic drugs are non-selective between normal and pathological tissue, and this poses a challenge regarding the strategy for treatment of tumors. To achieve sufficient antitumor activity for colorectal carcinoma, optimization of the therapeutic regimen is of great importance. We investigated the ability of oxaliplatin long-circulating liposomes (PEG-liposomal L-oHP) to provide an improved therapeutic index of colorectal carcinoma. Results: We determined that PEG- liposomes conjugated with cells at 2 h, with a mean fluorescence intensity that was enhanced upon extended induction time. The PEG-liposomal L-oHP induced a significant apoptotic response as compared with free L-oHP, 23.21% ± 3.38% vs. 16.85% ± 0.98%, respectively. Fluorescence imaging with In-Vivo Imaging demonstrated that PEG- liposomes specifically targeted tumour tissue. After intravenous injections of PEGliposomal L-oHP or free L-oHP, the tumour volume suppression ratio was 26.08% ± 12.43% and 18.19% ± 7.09%, respectively, the percentage increased life span (ILS%) was 45.36% and 76.19%, respectively, and Bcl-2, Bax mRNA and protein expression in tumour tissue was 0.27-fold vs. 0.88-fold and 1.32-fold vs. 1.61-fold compared with free L-oHP, respectively. Conclusion: The PEG-liposomal L-oHP exhibited a tendency to target tumour tissue and demonstrated a significantly greater impact on apoptosis compared to free oxaliplatin. [ABSTRACT FROM AUTHOR]