Microwave Irradiation Effects on Random Telegraph Signal in a MOSFET

We report on the change of the characteristic times of the random telegraph signal (RTS) in a MOSFET operated under microwave irradiation up to 40 GHz as the microwave field power is raised. The effect is explained by considering the time dependency of the transition probabilities due to a harmonic voltage generated by the microwave field that couples with the wires connecting the MOSFET. From the dc current excited into the MOSFET by the microwave field we determine the corresponding equivalent drain voltage. The RTS experimental data are in agreement with the prediction obtained with the model, making use of the voltage data measured with the independent dc microwave induced current. We conclude that when operating a MOSFET under microwave irradiation, as in single spin resonance detection, one has to pay attention into the effects related to microwave irradiation dependent RTS changes.Comment: 3 pages, 4 figure

Memristive neural network for on-line learning and tracking with brain-inspired spike timing dependent plasticity

Brain-inspired computation can revolutionize information technology by introducing machines capable of recognizing patterns (images, speech, video) and interacting with the external world in a cognitive, humanlike way. Achieving this goal requires first to gain a detailed understanding of the brain operation, and second to identify a scalable microelectronic technology capable of reproducing some of the inherent functions of the human brain, such as the high synaptic connectivity (~104) and the peculiar time-dependent synaptic plasticity. Here we demonstrate unsupervised learning and tracking in a spiking neural network with memristive synapses, where synaptic weights are updated via brain-inspired spike timing dependent plasticity (STDP). The synaptic conductance is updated by the local time-dependent superposition of pre-and post-synaptic spikes within a hybrid one-transistor/one-resistor (1T1R) memristive synapse. Only 2 synaptic states, namely the low resistance state (LRS) and the high resistance state (HRS), are sufficient to learn and recognize patterns. Unsupervised learning of a static pattern and tracking of a dynamic pattern of up to 4 Ã\u97 4 pixels are demonstrated, paving the way for intelligent hardware technology with up-scaled memristive neural networks

Time Dependent Inelastic Emission and Capture of Localized Electrons in Si n-MOSFETs Under Microwave Irradiation

Microwave irradiation causes voltage fluctuations in solid state nanodevices. Such an effect is relevant in atomic electronics and nanostructures for quantum information processing, where charge or spin states are controlled by microwave fields and electrically detected. Here the variation of the characteristic times of the multiphonon capture and emission of a single electron by an interface defect in submicron MOSFETs is calculated and measured as a function of the microwave power, whose frequency of the voltage modulation is assumed to be large if compared to the inverse of the characteristic times. The variation of the characteristic times under microwave irradiation is quantitatively predicted from the microwave frequency dependent stationary current generated by the voltage fluctuations itself. The expected values agree with the experimental measurements. The coupling between the microwave field and either one or two terminals of the device is discussed. Some consequences on nanoscale device technology are drawn.Comment: 8 Figure

Economic and Social Impacts of Olive Quick Decline Syndrome: Analysing Data From the Italian Farm Accountancy Network

The rapid spread of Xylella fastidiosa subsp. pauca (Xfp) in the Salento area (Apulia region, southern Italy) has caused extensive socio-economic damage to the olive oil supply chain. This research evaluates the impact of the ‘Xfp treatment’ on selected economic and social variables using a counterfactual approach. We applied propensity score matching and the difference-in-difference estimator to a sample of Italian Farm Accountancy Data Network panel olive-growing farms. The study compared the outcomes of farms affected by the Xfp invasion before (2008–2012) and after (2017–2021), with a control group unaffected by Xfp. The results showed that the socio-economic performance of Salento's olive-growing farms is lower than unaffected farms outside the region but comparable to similarly affected farms. Regarding the economic impact of Xfp, the Gross Operating Margin had an Average Treatment Effect on the Treated of around −€837 per hectare, indicating a reduction in profitability, amounting to a total loss of €132 million across the infected area. Social indicators also showed the effects of Xfp, evident in the reduction of total working hours and work units employed on Salento olive farms. The decrease was −7 h/ha, resulting in a total loss of 1,050,000 h across the entire infected area in Apulia (approximately 150,000 ha). These findings have policy implications, because they can assist policymakers in establishing a compensation budget for Apulian olive growers affected by Xfp. Identifying fair compensation is crucial for providing financial and technical support to help farmers convert their crops or adopt alternative agricultural practices

Learning of spatiotemporal patterns in a spiking neural network with resistive switching synapses

The human brain is a complex integrated spatiotemporal system, where space (which neuron fires) and time (when a neuron fires) both carry information to be processed by cognitive functions. To parallel the energy efficiency and computing functionality of the brain, methodologies operating over both the space and time domains are thus essential. Implementing spatiotemporal functions within nanoscale devices capable of synaptic plasticity would contribute a significant step toward constructing a large-scale neuromorphic system that emulates the computing and energy performances of the human brain. We present a neuromorphic approach to brain-like spatiotemporal computing using resistive switching synapses. To process the spatiotemporal spike pattern, time-coded spikes are reshaped into exponentially decaying signals that are fed to a McCulloch-Pitts neuron. Recognition of spike sequences is demonstrated after supervised training of a multiple-neuron network with resistive switching synapses. Finally, we show that, due to the sensitivity to precise spike timing, the spatiotemporal neural network is able to mimic the sound azimuth detection of the human brain

Postcycling Degradation in Metal-Oxide Bipolar Resistive Switching Memory

Resistive switching memory (RRAM) features many optimal properties for future memory applications that make RRAM a strong candidate for storage-class memory and embedded nonvolatile memory. This paper addresses the cycling-induced degradation of RRAM devices based on a HfO2 switching layer. We show that the cycling degradation results in the decrease of several RRAM parameters, such as the resistance of the low-resistance state, the set voltage Vset, the reset voltage Vreset, and others. The degradation with cycling is further attributed to enhanced ion mobility due to defect generation within the active filament area in the RRAM device. A distributed-energy model is developed to simulate the degradation kinetics and support our physical interpretation. This paper provides an efficient methodology to predict device degradation after any arbitrary number of cycles and allows for wear leveling in memory array

Stochastic Learning in Neuromorphic Hardware via Spike Timing Dependent Plasticity with RRAM Synapses

Hardware processors for neuromorphic computing are gaining significant interest as they offer the possibility of real in-memory computing, thus by-passing the limitations of speed and energy consumption of the von Neumann architecture. One of the major limitations of current neuromorphic technology is the lack of bio-realistic and scalable devices to improve the current design of artificial synapses and neurons. To overcome these limitations, the emerging technology of resistive switching memory has attracted wide interest as a nano-scaled synaptic element. This paper describes the implementation of a perceptron-like neuromorphic hardware capable of spike-timing dependent plasticity (STDP), and its operation under stochastic learning conditions. The learning algorithm of a single or multiple patterns, consisting of either static or dynamic visual input data, is described. The impact of noise is studied with respect to learning efficiency (false fire, true fire) and learning time. Finally, the impact of stochastic learning rule, such as the inversion of the time dependence of potentiation and depression in STDP, is considered. Overall, the work provides a proof of concept for unsupervised learning by STDP in memristive networks, providing insight into the dynamics of stochastic learning and supporting the understanding and design of neuromorphic networks with emerging memory devices