Analysis Of Random Telegraph Noise Research Articles

Analysis of random telegraph noise in resistive memories: The case of unstable filaments

Through Random Telegraph Noise (RTN) analysis, valuable information can be provided about the role of defect traps in fine tuning and reading of the state of a nanoelectronic device. However, time domain analysis techniques exhibit their limitations in case where unstable RTN signals occur. These instabilities are a common issue in Multi-Level Cells (MLC) of resistive memories (ReRAM), when the tunning protocol fails to find a perfectly stable resistance state, which in turn brings fluctuations to the RTN signal especially in long time measurements and cause severe errors in the estimation of the distribution of time constants of the observed telegraphic events, i.e., capture/emission of carriers from traps. In this work, we analyze the case of the unstable filaments in silicon nitride-based ReRAM devices and propose an adaptive filter implementing a moving-average detrending method in order to flatten unstable RTN signals and increase sufficiently the accuracy of the conducted measurements. The τe and τc emission/capture time constants of the traps, respectively, are then calculated and a cross-validation through frequency domain analysis (Lorentzian fitting) was performed proving that the proposed method is accurate.

Extraction of Trap Energy Distribution in Proton-Irradiated Focal Plane Arrays Using a High-Precision Random Telegraph Noise Detection Algorithm

Random telegraph noise (RTN) is a type of low-frequency noise that degrades the sensitivity of focal plane arrays (FPAs) and other electronics. Careful analysis of RTN provides information about the material defects generating this noise and ultimately may lead to a solution to eliminate this noise source from the optoelectronic imaging system. The focus of this effort is the definition and demonstration of a simple histogram-based detection algorithm, which evaluates the shape of the pixel output histogram in order to detect RTN pixels and extract the energy levels of the defects. The algorithm sensitivity and precision statistics are benchmarked as a function of the detection threshold. Applying the algorithm to a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1280\times1024$ </tex-math></inline-formula> infrared FPA irradiated with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$3.2\times 10^{11}$ </tex-math></inline-formula> protons/cm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> (63 MeV) shows a maximum 84% increase in the number of pixels exhibiting RTN from preradiation to post-300 K anneal. The distribution of preradiation trap energies shows clusters at 13 meV above the Fermi level and 14 and 44 meV below the Fermi level. The radiation-induced defects, however, exhibit a different energy distribution with an increased concentration at the Fermi level, suggesting different energy distributions for the traps created during the manufacturing process compared to those created by proton radiation.

Neural network based analysis of random telegraph noise in resistive random access memories

The characterization of random telegraph noise (RTN) signals in resistive random access memories (RRAM) is a challenge. The inherent stochastic operation of these devices, much different to what is seen in other electron devices such as MOSFETs, diodes, etc, makes this issue more complicated from the mathematical viewpoint. Nevertheless, the accurate modeling of these type of signals is essential for their use in digital and analog applications. RTN signals are revealed to be linked to the emission and capture of electrons by traps close to the conductive filament that can influence resistive switching operation in RRAMs. RTN features depend on the number of active traps, on the interaction between these traps at different times, on the occurrence of anomalous effects, etc Using a new representation technique, the locally weighted time lag plot (LWTLP), a highly efficient method in terms of computation, data from current–time (I–t) traces can be represented with a pattern that allows the analysis of important RTN signal features. In addition, self-organizing maps, a neural network devoted to clustering, can be employed to perform an automatic classification of the RTN traces that have similar LWTLP patterns. This pattern analysis allows a better understanding of RTN signals and the physics underlying them. The new technique presented can be performed in a reasonable computing time and it is particularly adequate for long (I–t) traces. We introduce here this technique and the most important results that can be drawn when applied to long RTN traces experimentally obtained in RRAMs.

Guidelines for a Reliable Analysis of Random Telegraph Noise in Electronic Devices

In this paper, we propose new guidelines for the analysis of random telegraph noise (RTN) in electronic devices. Starting from an in-depth understanding of RTN signal characteristics, we will identify the correct measurement conditions to enable RTN analysis as a characterization tool for electronic devices. The estimate of RTN statistical parameters may indeed strongly depend on the choice of measurement conditions. We will carefully consider both the measurement limits and the extraction process constraints to devise a strategy to identify RTN signals measured in conditions allowing a meaningful estimation of their parameters. The proposed strategy will be tested on a variety of different RTN signals and operating conditions.

Single vacancy defect spectroscopy on HfO2 using random telegraph noise signals from scanning tunneling microscopy

Random telegraph noise (RTN) measurements are typically carried out at the device level using standard probe station based electrical characterization setup, where the measured current represents a cumulative effect of the simultaneous response of electron capture/emission events at multiple oxygen vacancy defect (trap) sites. To better characterize the individual defects in the high-κ dielectric thin film, we propose and demonstrate here the measurement and analysis of RTN at the nanoscale using a room temperature scanning tunneling microscope setup, with an effective area of interaction of the probe tip that is as small as 10 nm in diameter. Two-level and multi-level RTN signals due to single and multiple defect locations (possibly dispersed in space and energy) are observed on 4 nm HfO2 thin films deposited on n-Si (100) substrate. The RTN signals are statistically analyzed using the Factorial Hidden Markov Model technique to decode the noise contribution of more than one defect (if any) and estimate the statistical parameters of each RTN signal (i.e., amplitude of fluctuation, capture and emission time constants). Observation of RTN at the nanoscale presents a new opportunity for studies on defect chemistry, single-defect kinetics and their stochastics in thin film dielectric materials. This method allows us to characterize the fast traps with time constants ranging in the millisecond to tens of seconds range.

Study of Hot-Carrier-Induced Traps in Nanoscale UTBB FD-SOI MOSFETs by Low-Frequency Noise Measurements

The hot carrier (HC)-induced traps in nanoscale fully depleted ultrathin body and buried oxide nMOSFETs are investigated by low-frequency noise (LFN) measurements in the frequency and time domains. The measured noise spectra are composed of 1/f and Lorentzian-type components. The Lorentzian noise is due to either generation–recombination noise or random telegraph noise (RTN). Based on the LFN results, the effect of the HC stress on fully depleted silicon-on-insulator MOSFETs is investigated after short- and long-time stress. The capture and emission time constants responsible for the RTN noise were calculated as the average duration time of the high/low drain current state, respectively. Analysis of RTN traps detected in fresh and HC-stressed devices indicates that the RTN amplitude is uncorrelated to the trap time constants, i.e., the impact of the trap depth from the interface is masked by that of the trap location over the channel. The overall results lead to an analytical expression for the RTN amplitude, enabling to predict the RTN changes from the subthreshold to the above-threshold region.

Statistical analysis of random telegraph noise in HfO2-based RRAM devices in LRS

In this work, we present a thorough statistical characterization of Random Telegraph Noise (RTN) in HfO2-based Resistive Random Access Memory (RRAM) cells in Low Resistive State (LRS). Devices are tested under a variety of operational conditions. A Factorial Hidden Markov Model (FHMM) analysis is exploited to extrapolate the properties of the traps causing multi-level RTN in LRS. The trapping and de-trapping of charge carriers into/out of defects located in the proximity of the conductive filament results in a shielding effect on a portion of the conductive filament, leading to the observed RTN current fluctuations. It is found that both oxygen vacancies and oxygen ions defects may be responsible for the observed RTN. The variations of the current observed at subsequent set/reset cycles are instead attributed to the stochastic variations in the filament due to oxidation/reduction processes during reset and set operations, respectively.

Analysis of random telegraph noise observed in semiconducting carbon nanotube quantum dots

We have investigated random telegraph noise (RTN) observed in individual semiconducting carbon nanotubes (CNTs) in Coulomb-blockade regime. RTN characteristics are studied as a function of gate-voltage and drain–source voltage. Our results are explained by the capture and emission of carriers by charge traps in the vicinity of CNTs. Because of the large RTN amplitude, often greater than ∼50% of the total current, RTN measurements can be developed into an effective tool to estimate the trap energy and the location of the trap in CNT quantum dot devices.

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

&#x0D; This paper presents a new technique to analyze the characteristics of multi-level random telegraph noise (RTN). RTN is defined as an abrupt switching of either the current or the voltage between discrete values as a result of trapping/de-trapping activity. RTN signal properties are deduced exploiting a factorial hidden Markov model (FHMM). The proposed method considers the measured multi-level RTN as a superposition 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.&#x0D;

Magnetization reversal in sub-100 nm magnetic tunnel junctions with ultrathin MgO barrier biased along the hard axis

We report on room temperature magnetoresistance and low frequency noise in sub-100 nm elliptic CoFeB/MgO/CoFeB magnetic tunnel junctions with ultrathin (0.9 nm) barriers. For magnetic fields applied along the hard axis, we observe current induced magnetization switching between the antiparallel and parallel alignments at dc current densities as low as 4 × 106 A/cm2. We attribute the low value of the critical current to the influence of localized reductions in the tunnel barrier, which affects the current distribution. The analysis of random telegraph noise, which appears in the field interval near a magnetization switch, provides an estimate to the dimension of the pseudo pinholes that trigger the magnetization switching via local spin torque. Micromagnetic simulations qualitatively and quantitatively reproduce the main experimental observations.

Three-Dimensional Electrostatics- and Atomistic Doping-Induced Variability of RTN Time Constants in Nanoscale MOS Devices—Part II: Spectroscopic Implications

This paper investigates the impact of 3-D electrostatics and atomistic doping on the spectroscopic analysis of random telegraph noise (RTN) traps in nanoscale MOS devices. Using the numerical model and the template decananometer Flash cell presented in Part I of this paper, the gate bias dependence of the capture and emission time constants of oxide traps is shown to largely depend on the trap position over the channel, both in the subthreshold and in the on-state regime. This compromises the accuracy of any 1-D method for trap spectroscopy based on the time constants analysis and, due to the randomness in trap position and dopant placement in the substrate, calls into question the possibility for any accurate trap spectroscopy in nanoscale devices. Finally, the possibility to extract any information on the trap depth from the fluctuation amplitude of RTN waveforms is shown to be precluded by the large statistical spread of the amplitude itself, resulting in its negligible correlation with the trap position in the oxide and with the waveform time constants.

Evidence for nonlinear screening and enhancement of scattering by a single Coulomb impurity for dielectrically confined electrons in InAs nanowires

The conductance due to scattering by a single repulsive Coulomb impurity is measured as a function of gate voltage in backgated InAs nanowires by analysis of random telegraph noise. Comparison with a quantum mechanical theory for carrier response and scattering reveals that the large dielectric mismatch between the nanowire and its surroundings enhances the Coulomb interaction, produces a nonlinear screening process that weakens dielectric response, and enhances the self-consistent Coulomb-impurity barrier of a single repulsive impurity, as nanowire diameter is reduced. Consequently, the scattering rate by such an impurity is enhanced by nearly two orders magnitude for 30 nm diameter InAs nanowires. A dramatic asymmetry of scattering by repulsive and attractive impurities, where the latter produce majority carriers, explain how a single repulsive impurity can control the conductance of a 1 $\ensuremath{\mu}$m long InAs nanowire. Relevance to proposed nanoelectronic and sensing devices is discussed.

Static Analysis of Random Telegraph Noise in a 45-nm Channel Length Conventional MOSFET Device: Threshold Voltage and ON-Current Fluctuations

In this paper, we investigate the threshold voltage and ON-current fluctuations due to the presence of charged traps located in the middle portion of the channel when the trap is moved from the source end to the drain end of the channel in a 45-nm technology node device with an effective channel length of 35 nm. Our thorough investigations suggest that the threshold voltage fluctuation and its standard deviation are much larger than the ON-current fluctuation since in the ON-state screening effectively reduces the strength of the trap Coulomb potential, which is not the case in the OFF-state. We believe that this is a first study that simultaneously investigates the effects of random dopant and random telegraph noise fluctuations that utilizes particle-based device simulators that correctly account for the long-range and the short-range Coulomb interaction. Unique feature of the approach is the proper incorporation, in a self-consistent manner, of the short-range Coulomb interaction via the real-space molecular dynamics routine. Indeed, Vasileska has pioneered this technique back in 1996. We also find that studies that do not account for the short-range Coulomb interaction correctly miss important feature that the threshold voltage standard deviations are not independent upon the position of the trap in the channel but are strongly correlated with it when the trap is located in the middle section of the source end of the channel. This suggests that an approach that correctly accounts for the short-range Coulomb interaction is a must when modeling either random dopant or random trap fluctuations in both the threshold voltage and the ON-current.

Analysis of random telegraph noise in spin valve heads with ultra-thin free layers

Random telegraph noise (RTN) in spin valve heads with ultra-thin free layers is analyzed in both time and frequency domain. RTN is characterized by random fluctuation between two meta-stable states and is attributed to thermally activated domain instability. Lifetime of each meta-stable state is changes with bias current, with both being equal when RTN amplitude peaks while asymmetry is near zero. The lifetime at equilibrium can be quantified by the flatness of RTN spectra and is correlated with the normalized peak area under RTN amplitude versus bias current curve. This area scales with the energy barrier associated with RTN. With the same RTN peak area, lifetime at equilibrium is shorter for heads with thinner free layers but otherwise the same structure. Impact on reader instability for ultra-high areal density recording is discussed.

Analysis of random telegraph noise in large-area amorphous double-barrier structures.

We have measured the temporal behavior of the perpendicular current through large-area (≃0.25 mm 2 ) double-barrier structures fabricated from n + -type a-Si:H and quasistoichiometric a-SiN x :H. The current switches randomly between distinct values with an amplitude between 0.5% and 10% of the total current. This behavior is reminiscent of random telegraph noise (RTN) observed in small-area (<1 μM 2 ) devices. The power spectra of the RTN can be fitted by a superposition of Lorentzians from which effective switching rates can be deduced. They are thermally activated and exhibit a dependence on the voltage applied to the sample