Resistive switching devices with improved control of oxygen vacancies dynamics

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Investigation on the Stabilizing Effect of Titanium in HfO2-Based Resistive Switching Devices With Tungsten Electrode

Resistive switching (RS) devices, also referred to as resistive random access memories (ReRAMs), rely on a working principle based on the change of electrical resistance following proper external electrical stimuli. Since the demonstration of the first resistive memory based on a binary transition metal oxide (TMO) enclosed in a metal–insulator–metal (MIM) structure, this class of devices has been considered a key player for simple and low-cost memories. However, successful large-scale integration with standard complementary metal–oxide–semiconductor (CMOS) technologies still needs systematic investigations. In this work, we examine the beneficial effect titanium has when employed as a buffer layer between CMOS-compatible materials like hafnium dioxide and tungsten. Hindering the tungsten oxidation, Ti provides RS stabilization and allows getting faster responses from the devices. Through an extensive comparative study, the effect of both thickness and composition of Ti-based buffer layers is investigated. The reported results show how titanium can be effectively employed to stabilize and tailor the RS behavior of the devices, and they may open the way to the definition of new design rules for ReRAM–CMOS integration. Moreover, the gradual switching and the response speed tunability observed employing titanium might also extend the domain of interest of these results to brain-inspired computing applications

Review of open neuromorphic architectures and a first integration in the RISC-V PULP platform

Although initially conceived as a tool to empower neuroscientific research by emulating and simulating the human brain, Spiking Neural Networks (SNNs), also known as third generation neural networks, are gaining popularity for their low-power and sparse data processing capabilities. These attributes are valuable for power-constrained edge and Internet of Things (IoT) applications. Several open-source FPGA and ASIC neuromorphic processors have been developed to explore thisfield, although they often require additional computing elements to manage data and communications. In this work, we review recent open-source neuromorphic architectures and the PULP ecosystem. We then present an integration of the ReckOn digitalneuromorphic processor with the PULPissimo RISC-V single core microcontroller to enable edge IoT applications. Our integrated design is validated through QuestaSim hardware simulation. Through this integration of low-power neuromorphic and RISC-V processors, we focus on the promising potential of SNNs for optimizing edge IoT systems constrained by power budgets and data sparsity

Spike encoding techniques for IoT time-varying signals benchmarked on a neuromorphic classification task

Spiking Neural Networks (SNNs), known for their potential to enable low energy consumption and computational cost, can bring significant advantages to the realm of embedded machine learning for edge applications. However, input coming from standard digital sensors must be encoded into spike trains before it can be elaborated with neuromorphic computing technologies. We present here a detailed comparison of available spike encoding techniques for the translation of time-varying signals into the event-based signal domain, tested on two different datasets both acquired through commercially available digital devices: the Free Spoken Digit dataset (FSD), consisting of 8-kHz audio files, and the WISDM dataset, composed of 20-Hz recordings of human activity through mobile and wearable inertial sensors. We propose a complete pipeline to benchmark these encoding techniques by performing time-dependent signal classification through a Spiking Convolutional Neural Network (sCNN), including a signal preprocessing step consisting of a bank of filters inspired by the human cochlea, feature extraction by production of a sonogram, transfer learning via an equivalent ANN, and model compression schemes aimed at resource optimization. The resulting performance comparison and analysis provides a powerful practical tool, empowering developers to select the most suitable coding method based on the type of data and the desired processing algorithms, and further expands the applicability of neuromorphic computational paradigms to embedded sensor systems widely employed in the IoT and industrial domains

Human activity recognition: suitability of a neuromorphic approach for on-edge AIoT applications

Human activity recognition (HAR) is a classification problem involving time-dependent signals produced by body monitoring, and its application domain covers all the aspects of human life, from healthcare to sport, from safety to smart environments. As such, it is naturally well suited for on-edge deployment of personalized point-of-care (POC) analyses or other tailored services for the user. However, typical smart and wearable devices suffer from relevant limitations regarding energy consumption, and this significantly hinders the possibility for successful employment of edge computing for tasks like HAR. In this paper, we investigate how this problem can be mitigated by adopting a neuromorphic approach. By comparing optimized classifiers based on traditional deep neural network (DNN) architectures as well as on recent alternatives like the Legendre Memory Unit (LMU), we show how spiking neural networks (SNNs) can effectively deal with the temporal signals typical of HAR providing high performances at a low energy cost. By carrying out an application-oriented hyperparameter optimization, we also propose a methodology flexible to be extended to different domains, to enlarge the field of neuro-inspired classifier suitable for on-edge artificial intelligence of things (AIoT) applications

Smart Traffic Light Control on Edge in IOT-Regulated Intersections

Traffic is a well-known everyday problem that standard traffic lights controllers can struggle to deal with, especially in highly populated cities, resulting in congestion at the intersections and the consequent formation of queues.Smart traffic lights management, relying on Internet of Things (IoT) concepts and devices, may be adopted to mitigate this phenomenon. In this paper, we propose a Smart Intersection for Smart Traffic (SIST) regulated model using the max-pressure controller algorithm to dynamically modulate the duration of traffic lights, implemented on real-time embedded hardware and using data coming from local sensors and the IoT network.Compared to standard, fixed-duration control schemes, the dynamically IoT-regulated SIST model ensures overall reduction of the queue lengths, resulting in improved prevention of link overload by about 7% compared to the most favorable fixed-duration model

P3HT Processing Study for In-Liquid EGOFET Biosensors: Effects of the Solvent and the Surface

In-liquid biosensing is the new frontier of health and environment monitoring. A growing number of analytes and biomarkers of interest correlated to different diseases have been found, and the miniaturized devices belonging to the class of biosensors represent an accurate and cost-effective solution to obtaining their recognition. In this study, we investigate the effect of the solvent and of the substrate modification on thin films of organic semiconductor Poly(3-hexylthiophene) (P3HT) in order to improve the stability and electrical properties of an Electrolyte Gated Organic Field Effect Transistor (EGOFET) biosensor. The studied surface is the relevant interface between the P3HT and the electrolyte acting as gate dielectric for in-liquid detection of an analyte. Atomic Force Microscopy (AFM) and X-ray Photoelectron Spectroscopy (XPS) characterizations were employed to study the effect of two solvents (toluene and 1,2-dichlorobenzene) and of a commercial adhesion promoter (Ti Prime) on the morphological structure and electronic properties of P3HT film. Combining the results from these surface characterizations with electrical measurements, we investigate the changes on the EGOFET performances and stability in deionized (DI) water with an Ag/AgCl gate electrode

WaLiN-GUI: a graphical and auditory tool for neuron-based encoding

Neuromorphic computing relies on spike-based, energy-efficient communication, inherently implying the need for conversion between real-valued (sensory) data and binary, sparse spiking representation. This is usually accomplished using the real valued data as current input to a spiking neuron model, and tuning the neuron's parameters to match a desired, often biologically inspired behaviour. We developed a tool, the WaLiN-GUI, that supports the investigation of neuron models and parameter combinations to identify suitable configurations for neuron-based encoding of sample-based data into spike trains. Due to the generalized LIF model implemented by default, next to the LIF and Izhikevich neuron models, many spiking behaviors can be investigated out of the box, thus offering the possibility of tuning biologically plausible responses to the input data. The GUI is provided open source and with documentation, being easy to extend with further neuron models and personalize with data analysis functions.Comment: 4 pages, 1 figur

Prescription appropriateness of anti-diabetes drugs in elderly patients hospitalized in a clinical setting: evidence from the REPOSI Register

Diabetes is an increasing global health burden with the highest prevalence (24.0%) observed in elderly people. Older diabetic adults have a greater risk of hospitalization and several geriatric syndromes than older nondiabetic adults. For these conditions, special care is required in prescribing therapies including anti- diabetes drugs. Aim of this study was to evaluate the appropriateness and the adherence to safety recommendations in the prescriptions of glucose-lowering drugs in hospitalized elderly patients with diabetes. Data for this cross-sectional study were obtained from the REgistro POliterapie-Società Italiana Medicina Interna (REPOSI) that collected clinical information on patients aged ≥ 65 years acutely admitted to Italian internal medicine and geriatric non-intensive care units (ICU) from 2010 up to 2019. Prescription appropriateness was assessed according to the 2019 AGS Beers Criteria and anti-diabetes drug data sheets.Among 5349 patients, 1624 (30.3%) had diagnosis of type 2 diabetes. At admission, 37.7% of diabetic patients received treatment with metformin, 37.3% insulin therapy, 16.4% sulfonylureas, and 11.4% glinides. Surprisingly, only 3.1% of diabetic patients were treated with new classes of anti- diabetes drugs. According to prescription criteria, at admission 15.4% of patients treated with metformin and 2.6% with sulfonylureas received inappropriately these treatments. At discharge, the inappropriateness of metformin therapy decreased (10.2%, P < 0.0001). According to Beers criteria, the inappropriate prescriptions of sulfonylureas raised to 29% both at admission and at discharge. This study shows a poor adherence to current guidelines on diabetes management in hospitalized elderly people with a high prevalence of inappropriate use of sulfonylureas according to the Beers criteria

Clinical features and outcomes of elderly hospitalised patients with chronic obstructive pulmonary disease, heart failure or both

Background and objective: Chronic obstructive pulmonary disease (COPD) and heart failure (HF) mutually increase the risk of being present in the same patient, especially if older. Whether or not this coexistence may be associated with a worse prognosis is debated. Therefore, employing data derived from the REPOSI register, we evaluated the clinical features and outcomes in a population of elderly patients admitted to internal medicine wards and having COPD, HF or COPD + HF. Methods: We measured socio-demographic and anthropometric characteristics, severity and prevalence of comorbidities, clinical and laboratory features during hospitalization, mood disorders, functional independence, drug prescriptions and discharge destination. The primary study outcome was the risk of death. Results: We considered 2,343 elderly hospitalized patients (median age 81 years), of whom 1,154 (49%) had COPD, 813 (35%) HF, and 376 (16%) COPD + HF. Patients with COPD + HF had different characteristics than those with COPD or HF, such as a higher prevalence of previous hospitalizations, comorbidities (especially chronic kidney disease), higher respiratory rate at admission and number of prescribed drugs. Patients with COPD + HF (hazard ratio HR 1.74, 95% confidence intervals CI 1.16-2.61) and patients with dementia (HR 1.75, 95% CI 1.06-2.90) had a higher risk of death at one year. The Kaplan-Meier curves showed a higher mortality risk in the group of patients with COPD + HF for all causes (p = 0.010), respiratory causes (p = 0.006), cardiovascular causes (p = 0.046) and respiratory plus cardiovascular causes (p = 0.009). Conclusion: In this real-life cohort of hospitalized elderly patients, the coexistence of COPD and HF significantly worsened prognosis at one year. This finding may help to better define the care needs of this population