Hysteresis in memristors produces conduction inductance and conduction capacitance effects.

Memristors are devices in which the conductance state can be alternately switched between a high and a low value by means of a voltage scan. In general, systems involving a chemical inductor mechanism as solar cells, asymmetric nanopores in electrochemical cells, transistors, and solid state memristive devices, exhibit a current increase and decrease over time that generates hysteresis. By performing small signal ac impedance spectroscopy, we show that memristors, or any other system with hysteresis relying on the conductance modulation effect, display intrinsic dynamic inductor-like and capacitance-like behaviours in specific input voltage ranges. Both the conduction inductance and the conduction capacitance originate in the same delayed conduction process linked to the memristor dynamics and not in electromagnetic or polarization effects. A simple memristor model reproduces the main features of the transition from capacitive to inductive impedance spectroscopy spectra, which causes a nonzero crossing of current-voltage curves.

Hardware implementation of memristor-based artificial neural networks.

Artificial Intelligence (AI) is currently experiencing a bloom driven by deep learning (DL) techniques, which rely on networks of connected simple computing units operating in parallel. The low communication bandwidth between memory and processing units in conventional von Neumann machines does not support the requirements of emerging applications that rely extensively on large sets of data. More recent computing paradigms, such as high parallelization and near-memory computing, help alleviate the data communication bottleneck to some extent, but paradigm- shifting concepts are required. Memristors, a novel beyond-complementary metal-oxide-semiconductor (CMOS) technology, are a promising choice for memory devices due to their unique intrinsic device-level properties, enabling both storing and computing with a small, massively-parallel footprint at low power. Theoretically, this directly translates to a major boost in energy efficiency and computational throughput, but various practical challenges remain. In this work we review the latest efforts for achieving hardware-based memristive artificial neural networks (ANNs), describing with detail the working principia of each block and the different design alternatives with their own advantages and disadvantages, as well as the tools required for accurate estimation of performance metrics. Ultimately, we aim to provide a comprehensive protocol of the materials and methods involved in memristive neural networks to those aiming to start working in this field and the experts looking for a holistic approach.

Global fine-resolution data on springtail abundance and community structure.

Springtails (Collembola) inhabit soils from the Arctic to the Antarctic and comprise an estimated ~32% of all terrestrial arthropods on Earth. Here, we present a global, spatially-explicit database on springtail communities that includes 249,912 occurrences from 44,999 samples and 2,990 sites. These data are mainly raw sample-level records at the species level collected predominantly from private archives of the authors that were quality-controlled and taxonomically-standardised. Despite covering all continents, most of the sample-level data come from the European continent (82.5% of all samples) and represent four habitats: woodlands (57.4%), grasslands (14.0%), agrosystems (13.7%) and scrublands (9.0%). We included sampling by soil layers, and across seasons and years, representing temporal and spatial within-site variation in springtail communities. We also provided data use and sharing guidelines and R code to facilitate the use of the database by other researchers. This data paper describes a static version of the database at the publication date, but the database will be further expanded to include underrepresented regions and linked with trait data.

Influence of pH and dissolved oxygen control strategies on the performance of pilot-scale microalgae raceways using fertilizer or wastewater as the nutrient source.

Dissolved oxygen concentration and pH are controllable and cost-effective variables that determine the success of microalgae-related processes. The present study compares different control strategies for pH and dissolved oxygen in pilot-scale microalgae production systems. Two 80 m2 raceway reactors were used, one operated with freshwater plus fertilizer and the other with wastewater as the nutrient source. Both were in semi-continuous mode at a fixed dilution rate of 0.2 day-1. A comparison between the classical On-Off and more advanced pH control strategies, such as PI and Event-based control, was performed, focusing on biomass productivity and the influence of all the process parameters on microalgae growth; "No control" of pH was also assayed. The results show that Event-based control was the best algorithm when using freshwater plus fertilizer. In contrast, no significant differences were observed using the different control strategies when wastewater was the nutrient source. These experiments were performed through selective control strategy, prioritizing pH over dissolved oxygen; however, it was demonstrated that they did not allow to achieve satisfactory dissolved oxygen removal results, especially for the fertilizer system. After modifying the gas diffuser configuration and improving the mass transfer, independent on-off strategies have been developed, permitting effective control of both variables and increasing productivity by up to 20% in both systems. Concluding, a detailed analysis of the energy demand for each strategy implemented in terms of gas consumption and gas flow to biomass ratio is provided.

Long-term assessment of the nutrient recovery capacity and biomass productivity of Scenedesmus almeriensis in raceway reactors using unprocessed urban wastewater.

The present work aims to assess the treatment of unprocessed urban wastewater using the microalga Scenedesmus almeriensis. Two 12 m3 raceway reactors, one supplemented by wastewater and the second by chemical fertilizer, operating outdoors in a semi-continuous mode, were used for eight months. Results suggested that S. almeriensis can be produced in wastewater without affecting the photosynthetic apparatus reaching a productivity of 13 g·m-2·day-1 on average in both the systems. Furthermore, the nutrient content in terms of nitrogen, phosphorous and chemical oxygen demand of the wastewater was reduced under the European limitations during most of the period, with an average removal rate of 2.2, 0.2 and 3.0 g·m-2·day-1 respectively. Therefore, raceways demonstrated a high potential for microalgal production and successful biotreatment, proving robust and reliable. Finally, the effect of environmental conditions on biomass productivity of the clean system was evaluated in a model with high accuracy (R2 = 0.9, p = 0.0002).

Experimental and Modeling Study of Metal-Insulator Interfaces to Control the Electronic Transport in Single Nanowire Memristive Devices.

Memristive devices relying on redox-based resistive switching mechanisms represent promising candidates for the development of novel computing paradigms beyond von Neumann architecture. Recent advancements in understanding physicochemical phenomena underlying resistive switching have shed new light on the importance of an appropriate selection of material properties required to optimize the performance of devices. However, despite great attention has been devoted to unveiling the role of doping concentration, impurity type, adsorbed moisture, and catalytic activity at the interfaces, specific studies concerning the effect of the counter electrode in regulating the electronic flow in memristive cells are scarce. In this work, the influence of the metal-insulator Schottky interfaces in electrochemical metallization memory (ECM) memristive cell model systems based on single-crystalline ZnO nanowires (NWs) is investigated following a combined experimental and modeling approach. By comparing and simulating the electrical characteristics of single NW devices with different contact configurations and by considering Ag and Pt electrodes as representative of electrochemically active and inert electrodes, respectively, we highlight the importance of an appropriate choice of electrode materials by taking into account the Schottky barrier height and interface chemistry at the metal-insulator interfaces. In particular, we show that a clever choice of metal-insulator interfaces allows to reshape the hysteretic conduction characteristics of the device and to increase the device performance by tuning its resistance window. These results obtained from single NW-based devices provide new insights into the selection criteria for materials and interfaces in connection with the design of advanced ECM cells.

Fast Fitting of the Dynamic Memdiode Model to the Conduction Characteristics of RRAM Devices Using Convolutional Neural Networks.

In this paper, the use of Artificial Neural Networks (ANNs) in the form of Convolutional Neural Networks (AlexNET) for the fast and energy-efficient fitting of the Dynamic Memdiode Model (DMM) to the conduction characteristics of bipolar-type resistive switching (RS) devices is investigated. Despite an initial computationally intensive training phase the ANNs allow obtaining a mapping between the experimental Current-Voltage (I-V) curve and the corresponding DMM parameters without incurring a costly iterative process as typically considered in error minimization-based optimization algorithms. In order to demonstrate the fitting capabilities of the proposed approach, a complete set of I-Vs obtained from Y2O3-based RRAM devices, fabricated with different oxidation conditions and measured with different current compliances, is considered. In this way, in addition to the intrinsic RS variability, extrinsic variation is achieved by means of external factors (oxygen content and damage control during the set process). We show that the reported method provides a significant reduction of the fitting time (one order of magnitude), especially in the case of large data sets. This issue is crucial when the extraction of the model parameters and their statistical characterization are required.

Enhancing the Thermal Conductivity of Amorphous Carbon with Nanowires and Nanotubes.

The thermal conductivity of nanostructures can be obtained using atomistic classical Molecular Dynamics (MD) simulations, particularly for semiconductors where there is no significant contribution from electrons to thermal conduction. In this work, we obtain and analyze the thermal conductivity of amorphous carbon (aC) nanowires (NW) with a 2 nm radius and aC nanotubes (NT) with 0.5, 1 and 1.3 nm internal radii and a 2 nm external radius. The behavior of thermal conductivity with internal radii, temperature and density (related to different levels of sp3 hybridization), is compared with experimental results from the literature. Reasonable agreement is found between our modeling results and the experiments for aC films. In addition, in our simulations, the bulk conductivity is lower than the NW conductivity, which in turn is lower than the NT conductivity. NTs thermal conductivity can be tailored as a function of the wall thickness, which surprisingly increases when the wall thickness decreases. While the vibrational density of states (VDOS) is similar for bulk, NW and NT, the elastic modulus is sensitive to the geometrical parameters, which can explain the enhanced thermal conductivity observed for the simulated nanostructures.

A seasonal simulation approach for culture depth influence on the temperature for different characterized microalgae strains.

Irradiance and temperature are among the most important variables that affect microalgae growth, being both difficult to control in outdoor raceway reactors utilized for large-scale production of microalgae biomass. They are mainly a function of the location of the reactors, thus, producing certain strains of microalgae in inappropriate places conduces to the failure of the systems. To be able to determine important parameters of any microalgae strains on the performance of the culture, such as the influence of irradiance and temperature, is a powerful tool in decision-making processes. In addition, whatever the strain and location, operation strategies must be defined for each specific case, such as the imposed dilution rate and culture depth, both influencing the light availability and temperature of the culture as major variables determining the biomass productivity. In this paper, a simulation-based methodology is proposed to establish the influence of season and culture depth on the 1-year age irradiance and temperature of the culture, and thus on the biomass productivity of different microalgae strains. Up to five of the most frequently produced strains, such as Spirulina platensis, Chlorella vulgaris, Nannochloropsis gaditana, Isochrysis galbana, and Scenedesmus almeriensis have been considered. The challenge is to develop an easy-to-manage decision-making tool for the optimal design and operation of large-scale microalgae facilities. Especially, dates for microalgae production and culture depth at which the reactors must be operated will be provided, being valid for any microalgae strain. The proposed methodology will largely contribute to the risk of investment in this field, then to enlarge the relevance of the microalgae production industry.

A new control strategy to improve the mass transfer capacity and reduce air injection costs in raceway reactors.

Raceway reactors are still the most extensive technology for microalgae production. However, these reactors have some drawbacks, one of them being a low mass transfer capacity, which provokes dissolved oxygen accumulation and thus reduction of system performance. To overcome this problem, it is imperative to improve the photobioreactor design as well as the operating conditions. One solution is to maintain the dissolved oxygen below defined limits. In this work, a new control algorithm is proposed to improve the mass transfer capacity of raceway reactors while at the same time reducing air injection costs. The main idea of the proposed control approach is that only the necessary amount of airflow will be applied according to transfer capacity demand. This control strategy was first analyzed in simulation and compared with classical On/Off solutions, and subsequently evaluated in outdoor conditions in a photobioreactor of 80 m2.

Quantum Conductance in Memristive Devices: Fundamentals, Developments, and Applications.

Quantum effects in novel functional materials and new device concepts represent a potential breakthrough for the development of new information processing technologies based on quantum phenomena. Among the emerging technologies, memristive elements that exhibit resistive switching, which relies on the electrochemical formation/rupture of conductive nanofilaments, exhibit quantum conductance effects at room temperature. Despite the underlying resistive switching mechanism having been exploited for the realization of next-generation memories and neuromorphic computing architectures, the potentialities of quantum effects in memristive devices are still rather unexplored. Here, a comprehensive review on memristive quantum devices, where quantum conductance effects can be observed by coupling ionics with electronics, is presented. Fundamental electrochemical and physicochemical phenomena underlying device functionalities are introduced, together with fundamentals of electronic ballistic conduction transport in nanofilaments. Quantum conductance effects including quantum mode splitting, stability, and random telegraph noise are analyzed, reporting experimental techniques and challenges of nanoscale metrology for the characterization of memristive phenomena. Finally, potential applications and future perspectives are envisioned, discussing how memristive devices with controllable atomic-sized conductive filaments can represent not only suitable platforms for the investigation of quantum phenomena but also promising building blocks for the realization of integrated quantum systems working in air at room temperature.

Nano-Memristors with 4 mV Switching Voltage Based on Surface-Modified Copper Nanoparticles.

The development of memristors operating at low switching voltages <50 mV can be very useful to avoid signal amplification in many types of circuits, such as those used in bioelectronic applications to interact with neurons and nerves. Here, it is reported that 400 nm-thick films made of dalkyl-dithiophosphoric (DDP) modified copper nanoparticles (CuNPs) exhibit volatile threshold-type resistive switching (RS) at ultralow switching voltage of ≈4 mV. The RS is observed in small nanocells with a lateral size of <50 nm-2 , during hundreds of cycles, and with an ultralow variability. Atomistic calculations reveal that the switching mechanism is related to the modification of the Schottky barriers and insulator-to-metal transition when ionic movement is induced via external bias. The devices are also used to model integrate-and-fire neurons for spiking neural networks and it is concluded that circuits employing DDP-CuNPs consume around ten times less power than similar neurons implemented with a memristor that switches at 40 mV.

Connectome of memristive nanowire networks through graph theory.

Hardware implementation of neural networks represents a milestone for exploiting the advantages of neuromorphic-type data processing and for making use of the inherent parallelism associated with such structures. In this context, memristive devices with their analogue functionalities are called to be promising building blocks for the hardware realization of artificial neural networks. As an alternative to conventional crossbar architectures where memristive devices are organized with a top-down approach in a grid-like fashion, neuromorphic-type data processing and computing capabilities have been explored in networks realized according to the principle of self-organization similarity found in biological neural networks. Here, we explore structural and functional connectivity of self-organized memristive nanowire (NW) networks within the theoretical framework of graph theory. While graph metrics reveal the link of the graph theoretical approach with geometrical considerations, results show that the interplay between network structure and its capacity to transmit information is related to a phase transition process consistent with percolation theory. Also the concept of memristive distance is introduced to investigate activation patterns and the dynamic evolution of the information flow across the network represented as a memristive graph. In agreement with experimental results, the emergent short-term dynamics reveals the formation of self-selected pathways with enhanced transport characteristics connecting stimulated areas and regulating the trafficking of the information flow. The network capability to process spatio-temporal input signals can be exploited for the implementation of unconventional computing paradigms in memristive graphs that take into advantage the inherent relationship between structure and functionality as in biological systems.

SPICE Implementation of the Dynamic Memdiode Model for Bipolar Resistive Switching Devices.

This paper reports the fundamentals and the SPICE implementation of the Dynamic Memdiode Model (DMM) for the conduction characteristics of bipolar-type resistive switching (RS) devices. Following Prof. Chua's memristive devices theory, the memdiode model comprises two equations, one for the electron transport based on a heuristic extension of the quantum point-contact model for filamentary conduction in thin dielectrics and a second equation for the internal memory state related to the reversible displacement of atomic species within the oxide film. The DMM represents a breakthrough with respect to the previous Quasi-static Memdiode Model (QMM) since it describes the memory state of the device as a balance equation incorporating both the snapback and snapforward effects, features of utmost importance for the accurate and realistic simulation of the RS phenomenon. The DMM allows simple setting of the initial memory condition as well as decoupled modeling of the set and reset transitions. The model equations are implemented in the LTSpice simulator using an equivalent circuital approach with behavioral components and sources. The practical details of the model implementation and its modes of use are also discussed.

Standards for the Characterization of Endurance in Resistive Switching Devices.

Resistive switching (RS) devices are emerging electronic components that could have applications in multiple types of integrated circuits, including electronic memories, true random number generators, radiofrequency switches, neuromorphic vision sensors, and artificial neural networks. The main factor hindering the massive employment of RS devices in commercial circuits is related to variability and reliability issues, which are usually evaluated through switching endurance tests. However, we note that most studies that claimed high endurances >106 cycles were based on resistance versus cycle plots that contain very few data points (in many cases even <20), and which are collected in only one device. We recommend not to use such a characterization method because it is highly inaccurate and unreliable (i.e., it cannot reliably demonstrate that the device effectively switches in every cycle and it ignores cycle-to-cycle and device-to-device variability). This has created a blurry vision of the real performance of RS devices and in many cases has exaggerated their potential. This article proposes and describes a method for the correct characterization of switching endurance in RS devices; this method aims to construct endurance plots showing one data point per cycle and resistive state and combine data from multiple devices. Adopting this recommended method should result in more reliable literature in the field of RS technologies, which should accelerate their integration in commercial products.

On the Thermal Models for Resistive Random Access Memory Circuit Simulation.

Resistive Random Access Memories (RRAMs) are based on resistive switching (RS) operation and exhibit a set of technological features that make them ideal candidates for applications related to non-volatile memories, neuromorphic computing and hardware cryptography. For the full industrial development of these devices different simulation tools and compact models are needed in order to allow computer-aided design, both at the device and circuit levels. Most of the different RRAM models presented so far in the literature deal with temperature effects since the physical mechanisms behind RS are thermally activated; therefore, an exhaustive description of these effects is essential. As far as we know, no revision papers on thermal models have been published yet; and that is why we deal with this issue here. Using the heat equation as the starting point, we describe the details of its numerical solution for a conventional RRAM structure and, later on, present models of different complexity to integrate thermal effects in complete compact models that account for the kinetics of the chemical reactions behind resistive switching and the current calculation. In particular, we have accounted for different conductive filament geometries, operation regimes, filament lateral heat losses, the use of several temperatures to characterize each conductive filament, among other issues. A 3D numerical solution of the heat equation within a complete RRAM simulator was also taken into account. A general memristor model is also formulated accounting for temperature as one of the state variables to describe electron device operation. In addition, to widen the view from different perspectives, we deal with a thermal model contextualized within the quantum point contact formalism. In this manner, the temperature can be accounted for the description of quantum effects in the RRAM charge transport mechanisms. Finally, the thermometry of conducting filaments and the corresponding models considering different dielectric materials are tackled in depth.

The Vision of Managing for Pest-Resistant Landscapes: Realistic or Utopic?

Purpose of Review: Forest managers have long suggested that forests can be made more resilient to insect pests by reducing the abundance of hosts, yet this has rarely been done. The goal of our paper is to review whether recent scientific evidence supports forest manipulation to decrease vulnerability. To achieve this goal, we first ask if outbreaks of forest insect pests have been more severe in recent decades. Next, we assess the relative importance of climate change and forest management-induced changes in forest composition/structure in driving these changes in severity. Recent Findings: Forest structure and composition continue to be implicated in pest outbreak severity. Mechanisms, however, remain elusive. Recent research elucidates how forest compositional and structural diversity at neighbourhood, stand, and landscape scales can increase forest resistance to outbreaks. Many recent outbreaks of herbivorous forest insects have been unprecedented in terms of duration and spatial extent. Climate change may be a contributing factor, but forest structure and composition have been clearly identified as contributing to these unprecedented outbreaks. Summary: Current research supports using silviculture to create pest-resistant forest landscapes. However, the precise mechanisms by which silviculture can increase resistance remains uncertain. Further, humans tend to more often create pest-prone forests due to political, economic, and human resistance to change and a short-sighted risk management perspective that focuses on reactive rather than proactive responses to insect outbreak threats. Future research efforts need to focus on social, political, cultural, and educational mechanisms to motivate implementation of proven ecological solutions if pest-resistant forests are to be favoured by management.

A new model to analyze the temperature effect on the microalgae performance at large scale raceway reactors.

In this study a simplified temperature model for raceway reactors is developed, allowing to determine the temperature of the microalgae culture as a function of reactor design and environmental conditions. The model considers the major phenomena taking place in raceway reactors, especially heat absorption by radiation and heat losses by evaporation among others. The characteristic parameters of the model have been calibrated using genetic algorithms, next being validated with a long set of more than 50 days covering different weather conditions. It is worth to highlight the use of the developed model as a tool to analyze the influence of the temperature on the performance of microalgae cultures at large scale. As example, the annual variation of the performance of up to five different microalgae strains has been determined by computing the temperature index, thus the normalized value of performance of whatever microalgae at the real temperature with respect to that achievable at optimal temperature can be established. Results confirm that only strains tolerant to wide ranges of temperature can be efficiently produced all the year around in large scale outdoor raceway reactors without additional temperature control systems.

Estudios dosimétricos de la calificación operacional y del comportamiento funcional de la instalación de irradiación semindustrial para la esterilización de un producto de uso médico / Dosimetric studies of the operational qualification and functional behavior of the semi-industrial irradiation facility for the sterilization of a product for medical use

Resumen En el presente trabajo se muestran los resultados obtenidos durante los estudios dosimétricos de las etapas de calificación operacional y del comportamiento funcional de la instalación de irradiación semindustrial de Cuba después de su remodelación y recarga, así como el proceso de radioesterilización de un producto de uso médico.

Abstract The present work shows the results obtained during the dosimetric studies of the operational qualification stages and the functional behavior of the semi-industrial irradiation facility in Cuba after its remodeling and recharging, as well as the radio-sterilization process of a product for medical use.

Memristors for Neuromorphic Circuits and Artificial Intelligence Applications.

Artificial Intelligence has found many applications in the last decade due to increased computing power. Artificial Neural Networks are inspired in the brain structure and consist in the interconnection of artificial neurons through artificial synapses in the so-called Deep Neural Networks (DNNs). Training these systems requires huge amounts of data and, after the network is trained, it can recognize unforeseen data and provide useful information. As far as the training is concerned, we can distinguish between supervised and unsupervised learning. The former requires labelled data and is based on the iterative minimization of the output error using the stochastic gradient descent method followed by the recalculation of the strength of the synaptic connections (weights) with the backpropagation algorithm. On the other hand, unsupervised learning does not require data labeling and it is not based on explicit output error minimization. Conventional ANNs can function with supervised learning algorithms (perceptrons, multi-layer perceptrons, convolutional networks, etc.) but also with unsupervised learning rules (Kohonen networks, self-organizing maps, etc.). Besides, another type of neural networks are the so-called Spiking Neural Networks (SNNs) in which learning takes place through the superposition of voltage spikes launched by the neurons. Their behavior is much closer to the brain functioning mechanisms they can be used with supervised and unsupervised learning rules. Since learning and inference is based on short voltage spikes, energy efficiency improves substantially. Up to this moment, all these ANNs (spiking and conventional) have been implemented as software tools running on conventional computing units based on the von Neumann architecture. However, this approach reaches important limits due to the required computing power, physical size and energy consumption. This is particularly true for applications at the edge of the internet. Thus, there is an increasing interest in developing AI tools directly implemented in hardware for this type of applications. The first hardware demonstrations have been based on Complementary Metal-Oxide-Semiconductor (CMOS) circuits and specific communication protocols. However, to further increase training speed andenergy efficiency while reducing the system size, the combination of CMOS neuron circuits with memristor synapses is now being explored. It has also been pointed out that the short time non-volatility of some memristors may even allow fabricating purely memristive ANNs. The memristor is a new device (first demonstrated in solid-state in 2008) which behaves as a resistor with memory and which has been shown to have potentiation and depression properties similar to those of biological synapses. In this Special Issue, we explore the state of the art of neuromorphic circuits implementing neural networks with memristors for AI applications.