Stochastic circuit breaker network model for bipolar resistance switching memories

Abstract:We present a stochastic model for resistance switching devices in which a square grid of resistor breakers plays the role of the insulator switching layer. The probability of breaker switching between two fixed resistance values, ROFF and RON, is determined by the corresponding voltage drop and thermal Joule heating. The breaker switching produces the overall device resistance change. Salient features of all the switching operations of bipolar resistance switching memories (RRAMs) are reproduced by the model and compared to a prototypical HfO2-based RRAM device. In particular, the need of a forming process that leads a fresh highly insulating device to a low resistance state (LRS) is captured by the model. Moreover, the model is able to reproduce the RESET process, which partially restores the insulating state through a gradual resistance transition as a function of the applied voltage and the abrupt nature of the SET process that restores the LRS. Furthermore, the multilevel capacity of a typical RRAM device obtained by tuning RESET voltage and SET compliance current is reproduced. The manuscript analyses the peculiar ingredients of the model and their inuence on the simulated current-voltage curves and, in addition, provides a detailed description of the mechanisms that connect the switching of the single breakers and that of the overall device

Arithmetic and geometric deformations of 3-folds

We show that mixed-characteristic and equi-characteristic small deformations of 3-dimensional canonical (resp. terminal) singularities with perfect residue field of characteristic $p>5$ are canonical (resp. terminal). We discuss applications to arithmetic and geometric families of 3-dimensional Fano varieties and minimal models with canonical singularities. Our results are contingent upon the existence of log resolutions of 4-folds.Comment: v3: 19 pages, minor corrections. To appear in Bull. London Math. So

Optical interferometry in the presence of large phase diffusion

Phase diffusion represents a crucial obstacle towards the implementation of high precision interferometric measurements and phase shift based communication channels. Here we present a nearly optimal interferometric scheme based on homodyne detection and coherent signals for the detection of a phase shift in the presence of large phase diffusion. In our scheme the ultimate bound to interferometric sensitivity is achieved already for a small number of measurements, of the order of hundreds, without using nonclassical light

Experimental estimation of one-parameter qubit gates in the presence of phase diffusion

We address estimation of one-parameter qubit gates in the presence of phase diffusion. We evaluate the ultimate quantum limits to precision, seek for optimal probes and measurements, and demonstrate an optimal estimation scheme for polarization qubits. An adaptive method to achieve optimal estimation in any working regime is also analyzed in details and experimentally implemented.Comment: revised version, to appear on PR

Estimation of spatial and temporal overlap in three ungulate species in a Mediterranean environment

AbstractInterspecific interactions are key drivers in structuring animal communities. Sympatric animals may show such behavioural patterns as the differential use of space and/or time to avoid competitive encounters. We took advantage of the ecological conditions of our study area, inhabited by different ungulate species, to investigate the spatial and temporal distribution ofCapreolus capreolus,Dama damaandSus scrofa. We estimated intraspecific interaction arising from the concomitant use of resources by using camera trapping. We collected 2741 videos with the three ungulates, which showed peculiar activity patterns. The three species were observed in all the habitat types of the study area over the four seasons, thus highlighting an evident spatial overlap. Moreover, our analysis demonstrated that the three species did not avoid each other through temporal segregation of their activities, rather showing a high overlap of daily activity rhythms, though with differences among the species and the seasons. Despite the high spatial and temporal overlap, the three species seemed to adopt segregation through fine-scale spatial avoidance: at an hourly level, the proportion of sites where the species were observed together was relatively low. This spatio-temporal segregation revealed complex and alternative behavioural strategies, which likely facilitated intra-guild sympatry among the studied species. Both temporal and spatio-temporal overlap reached the highest values in summer, when environmental conditions were more demanding. Given these results, we may presume that different drivers (e.g. temperature, human disturbance), which are likely stronger than interspecific interactions, affected activity rhythms and fine-scale spatial use of the studied species

Analog Memristive Synapse in Spiking Networks Implementing Unsupervised Learning

Emerging brain-inspired architectures call for devices that can emulate the functionality of biological synapses in order to implement new efficient computational schemes able to solve ill-posed problems. Various devices and solutions are still under investigation and, in this respect, a challenge is opened to the researchers in the field. Indeed, the optimal candidate is a device able to reproduce the complete functionality of a synapse, i.e. the typical synaptic process underlying learning in biological systems (activity-dependent synaptic plasticity). This implies a device able to change its resistance (synaptic strength, or weight) upon proper electrical stimuli (synaptic activity) and showing several stable resistive states throughout its dynamic range (analog behavior). Moreover, it should be able to perform spike timing dependent plasticity (STDP), an associative homosynaptic plasticity learning rule based on the delay time between the two firing neurons the synapse is connected to. This rule is a fundamental learning protocol in state-of-art networks, because it allows unsupervised learning. Notwithstanding this fact, STDP-based unsupervised learning has been proposed several times mainly for binary synapses rather than multilevel synapses composed of many binary memristors. This paper proposes an HfO2-based analog memristor as a synaptic element which performs STDP within a small spiking neuromorphic network operating unsupervised learning for character recognition. The trained network is able to recognize five characters even in case incomplete or noisy characters are displayed and it is robust to a device-to-device variability of up to +/-30%

Editorial: emerging technologies and systems for biologically plausible implementations of neural functions

No abstract available

Physical Implementation of a Tunable Memristor-based Chua's Circuit

Nonlinearity is a central feature in demanding computing applications that aim to deal with tasks such as optimization or classification. Furthermore, the consensus is that nonlinearity should not be only exploited at the algorithm level, but also at the physical level by finding devices that incorporate desired nonlinear features to physically implement energy, area and/or time efficient computing applications. Chaotic oscillators are one type of system powered by nonlinearity, which can be used for computing purposes. In this work we present a physical implementation of a tunable Chua's circuit in which the nonlinear part is based on a nonvolatile memristive device. Device characterization and circuit analysis serve as guidelines to design the circuit and results prove the possibility to tune the circuit oscillatory response by electrically programming the device.Comment: Accepted by IEEE 48th European Solid State Circuits Conference (ESSCIRC 2022