Factorial Hidden Markov Model analysis of Random Telegraph Noise in Resistive Random Access Memories

This paper presents a new technique to analyze the characteristics of multi-level random telegraph noise (RTN). RTN is dened as an abrupt switching of ei- ther the current or the voltage between discrete values as a result of trapping/de-trapping activity. RTN sig- nal properties are deduced exploiting a factorial hid- den Markov model (FHMM). The proposed method considers the measured multi-level RTN as a super- position of many two-levels RTNs, each represented by a Markov chain and associated to a single trap, and it is used to retrieve the statistical properties of each chain. These properties (i.e. dwell times and amplitude) are directly related to physical properties of each trap

Understanding the Reliability of Ferroelectric Tunnel Junction Operations using an Advanced Small-Signal Model

Ferroelectric technology is becoming ever more appealing for a variety of applications, especially analog neuromorphic computing. In this respect, elucidating the physical mechanisms occurring during device operation is of key importance to improve the reliability of ferroelectric devices. In this work, we investigate ferroelectric tunnel junctions (FTJs) consisting of a ferroelectric hafnium zirconium oxide (HZO) layer and an alumina (Al 2 O 3 ) layer by means of C-f and G-f measurements performed at multiple voltages and temperatures. For a dependable interpretation of the results, a new small signal model is introduced that goes beyond the state of the art by i) separating the role of the leakage in the two layers; ii) including the significant impact of the series impedance (that depends on the samples layout); iii) including the frequency dependence of the dielectric permittivity; iv) accounting for the fact that likely not the whole HZO volume crystallizes in the orthorhombic ferroelectric phase. The model correctly reproduces measurements taken on different devices in different conditions. Results highlight that the typical estimation method for interface trap density may be misleading

Clinical evidences of urea at low concentration

Urea is a hygroscopic molecule that, because of its moisturising properties, is topically used for the treatment of skin dryness at concentrations ranging from 2% to 12% in different formulations. Based on existing literature, low-concentration urea-containing products are effective in the treatment and/or prevention of xerosis in some skin disorders such as ichthyosis, atopic dermatitis and psoriasis, or unrelated to specific skin diseases. Generally, urea formulations at low concentration are well-tolerated and suited for the treatment of large skin areas, once or twice daily, even for a long period of time. At low concentrations stinging and burning sensation is rare and transient, whit no reported sensitisation despite its widespread use

Cerebellar BDNF promotes exploration and seeking for novelty

Approach system considered a motivational system that activates reward-seeking behavior is associated with exploration/impulsivity, whereas avoidance system considered an attentional system that promotes inhibition of appetitive responses is associated with active overt withdrawal. Approach and avoidance dispositions are modulated by distinct neurochemical profiles and synaptic patterns. However, the precise working of neurons and trafficking of molecules in the brain activity predisposing to approach and avoidance are yet unclear

Suffix Sorting via Matching Statistics

Funding Information: Academy of Finland grants 339070 and 351150 Publisher Copyright: © Zsuzsanna Lipták, Francesco Masillo, and Simon J. Puglisi.We introduce a new algorithm for constructing the generalized suffix array of a collection of highly similar strings. As a first step, we construct a compressed representation of the matching statistics of the collection with respect to a reference string. We then use this data structure to distribute suffixes into a partial order, and subsequently to speed up suffix comparisons to complete the generalized suffix array. Our experimental evidence with a prototype implementation (a tool we call sacamats) shows that on string collections with highly similar strings we can construct the suffix array in time competitive with or faster than the fastest available methods. Along the way, we describe a heuristic for fast computation of the matching statistics of two strings, which may be of independent interest.Peer reviewe

Deep Audio Analyzer: a Framework to Industrialize the Research on Audio Forensics

Deep Audio Analyzer is an open source speech framework that aims to simplify the research and the development process of neural speech processing pipelines, allowing users to conceive, compare and share results in a fast and reproducible way. This paper describes the core architecture designed to support several tasks of common interest in the audio forensics field, showing possibility of creating new tasks thus customizing the framework. By means of Deep Audio Analyzer, forensics examiners (i.e. from Law Enforcement Agencies) and researchers will be able to visualize audio features, easily evaluate performances on pretrained models, to create, export and share new audio analysis workflows by combining deep neural network models with few clicks. One of the advantages of this tool is to speed up research and practical experimentation, in the field of audio forensics analysis thus also improving experimental reproducibility by exporting and sharing pipelines. All features are developed in modules accessible by the user through a Graphic User Interface. Index Terms: Speech Processing, Deep Learning Audio, Deep Learning Audio Pipeline creation, Audio Forensics

A Bayesian model for a Pavlovian-instrumental transfer hypothesis

A Pavlovian conditioned stimulus (CS) associated with a reward can enhance an instrumental response directed to the same or other rewards. This effect is called Pavlovian-instrumental transfer (PIT). In recent years, lesion studies using rats have gained insight into its neural substrates dissociating between specific PIT (where CS and instrumental response share the same reward) and general PIT (where they do not) (Corbit and Balleine, 2005, 2011). Despite these advances, the functional differences between specific and general PIT and how Pavlovian cues interact with instrumental response are still not clear. Here we try to explain Pavlovian-instrumental transfer effects by using a latent causes Bayesian model. Previous work in the Pavlovian conditioning literature (Courville et al., 2005) suggests that during Pavlovian conditioning rats do not simply learn associations between two events (CS and reward); instead, they actually try to figure out the real hidden causes behind them by constructing a latent cause model. We expanded that view to include instrumental actions and so explain the interactions between Pavlovian conditioning and instrumental conditioning. Our model correctly reproduces both the presence of specific and general PIT and the absence of general PIT when the CS is associated to the reward of another instrumental action. By framing the PIT effects explanation in Bayesian terms, our model offers a new integrated view on their functional mechanisms and new testable predictions

Characterization and TCAD Modeling of Mixed-Mode Stress Induced by Impact Ionization in Scaled SiGe HBTs

We investigate the reliability of state-of-the-art SiGe heterojunction bipolar transistors (HBTs) in 55-nm technology under mixed-mode stress. We perform electrical characterization and implement a TCAD model calibrated on the measurement data to describe the increased base current degradation at different collector-base voltages. We introduce a simple and self-consistent simulation methodology that links the observed degradation trend to interface traps generation at the emitter/base spacer oxide ascribed to hot holes generated by impact ionization (II) in the collector/base depletion region. This effectively circumvents the limitations of commercial TCAD tools that do not allow II to be the driving force of the degradation. The approach accounts for self-heating and electric fields distribution allowing to reproduce measurement data including the deviation from the power-law behavior