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

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

Reliability of HfO2-Based Ferroelectric FETs: A Critical Review of Current and Future Challenges

Ferroelectric transistors (FeFETs) based on doped hafnium oxide (HfO2) have received much attention due to their technological potential in terms of scalability, highspeed, and low-power operation. Unfortunately, however, HfO2-FeFETs also suffer from persistent reliability challenges, specifically affecting retention, endurance, and variability. A deep understanding of the reliability physics of HfO2-FeFETs is an essential prerequisite for the successful commercialization of this promising technology. In this article, we review the literature about the relevant reliability aspects of HfO2-FeFETs. We initially focus on the reliability physics of ferroelectric capacitors, as a prelude to a comprehensive analysis of FeFET reliability. Then, we interpret key reliability metrics of the FeFET at the device level (i.e., retention, endurance, and variability) based on the physical mechanisms previously identified. Finally, we discuss the implications of device-level reliability metrics at both the circuit and system levels. Our integrative approach connects apparently unrelated reliability issues and suggests mitigation strategies at the device, circuit, or system level. We conclude this article by proposing a set of research opportunities to guide future development in this field

On the Modeling of the Donor/Acceptor Compensation Ratio in Carbon‐Doped GaN to Univocally Reproduce Breakdown Voltage and Current Collapse in Lateral GaN Power HEMTs

The intentional doping of lateral GaN power high electron mobility transistors (HEMTs) with carbon (C) impurities is a common technique to reduce buffer conductivity and increase breakdown voltage. Due to the introduction of trap levels in the GaN bandgap, it is well known that these impurities give rise to dispersion, leading to the so‐called “current collapse” as a collateral effect. Moreover, first‐principles calculations and experimental evidence point out that C introduces trap levels of both acceptor and donor types. Here, we report on the modeling of the donor/acceptor compensation ratio (CR), that is, the ratio between the density of donors and acceptors associated with C doping, to consistently and univocally reproduce experimental breakdown voltage (VBD) and current‐collapse magnitude (ΔICC). By means of calibrated numerical device simulations, we confirm that ΔICC is controlled by the effective trap concentration (i.e., the difference between the acceptor and donor densities), but we show that it is the total trap concentration (i.e., the sum of acceptor and donor densities) that determines VBD, such that a significant CR of at least 50% (depending on the technology) must be assumed to explain both phenomena quantitatively. The results presented in this work contribute to clarifying several previous reports, and are helpful to device engineers interested in modeling C‐doped lateral GaN power HEMTs

Editorial: Brain-inspired computing: Neuroscience drives the development of new electronics and artificial intelligence

Editorial for a special issue (i.e. "Research Topic") launched on the Journal Frontiers in Cellular Neuroscience - Section Cellular Neurophysiology

The effects of carbon on the bidirectional threshold voltage instabilities induced by negative gate bias stress in GaN MIS-HEMTs

In this paper, numerical device simulations are used to point out the possible contributions of carbon doping to the threshold voltage instabilities induced by negative gate bias stress in AlGaN/GaN metal–insulator–semiconductor high-electron mobility transistors. It is suggested that carbon can have a role in both negative and positive threshold voltage shifts, as a result of (1) the changes in the total negative charge stored in the carbon-related acceptor traps in the GaN buffer, and (2) the attraction of carbon-related free holes to the device surface and their capture into interface traps or recombination with gate-injected electrons. For a proper device optimization of carbon-doped MIS-HEMTs, it is therefore important to take these mechanisms into account, in addition to those related to defects in the gate dielectric volume and interface which are conventionally held responsible for threshold voltage instabilities

Biologically Plausible Information Propagation in a CMOS Integrate-and-Fire Artificial Neuron Circuit with Memristive Synapses

Neuromorphic circuits based on spikes are currently envisioned as a viable option to achieve brain-like computation capabilities in specific electronic implementations while limiting power dissipation given their ability to mimic energy efficient bio-inspired mechanisms. While several network architectures have been developed to embed in hardware the bio-inspired learning rules found in the biological brain, such as the Spike Timing Dependent Plasticity, it is still unclear if hardware spiking neural network architectures can handle and transfer information akin to biological networks. In this work, we investigate the analogies between an artificial neuron combining memristor synapses and rate-based learning rule with biological neuron response in terms of information propagation from a theoretical perspective. Bio-inspired experiments have been reproduced by linking the biological probability of release with the artificial synapses conductance. Mutual information and surprise have been chosen as metrics to evidence how, for different values of synaptic weights, an artificial neuron allows to develop a reliable and biological resembling neural network in terms of information propagation and analysi

Study of RRAM-Based Binarized Neural Networks Inference Accelerators Using an RRAM Physics-Based Compact Model

In-memory computing hardware accelerators for binarized neural networks based on resistive RAM (RRAM) memory technologies represent a promising solution for enabling the execution of deep neural network algorithms on resource-constrained devices at the edge of the network. However, the intrinsic stochasticity and nonidealities of RRAM devices can easily lead to unreliable circuit operations if not appropriately considered during the design phase. In this chapter, analysis and design methodologies enabled by RRAM physics-based compact models of LIM and mixed-signal BNN inference accelerators are discussed. As a use case example, the UNIMORE RRAM physics-based compact model calibrated on an RRAM technology from the literature, is used to determine the performance vs. reliability trade-offs of different in-memory computing accelerators: i) a logic-in-memory accelerator based on the material implication logic, ii) a mixed-signal BNN accelerator, and iii) a hybrid accelerator enabling both computing paradigms on the same array. Finally, the performance of the three accelerators on a BNN inference task is compared and benchmarked with the state of the art