Complex Random Telegraph Noise Research Articles

Local electric field perturbations due to trapping mechanisms at defects: What random telegraph noise reveals

As devices scale closer to the atomic size, a complete understanding of the physical mechanisms involving defects in high-κ dielectrics is essential to improve the performance of electron devices and to mitigate key reliability phenomena, such as Random Telegraph Noise (RTN). In fact, crucial aspects of defects in HfO2 are still under investigation (e.g., the presence of metastable states and their properties), but it is well known that oxygen vacancies (V+s) and oxygen ions (O0s) are the most abundant defects in HfO2. In this work, we use simulations to gain insights into the RTN that emerges when a constant voltage is applied across a TiN/(4 nm)HfO2/TiN stack. Signals exhibit different RTN properties over bias and, thus, appear to originate from different traps. Yet, we demonstrate that they can be instead promoted by the same O0s which change their capture (τc) and emission (τe) time constants with the applied bias, which, in turn, changes the extent of their electrostatic interactions with the traps that assist charge transport (V+s). For a certain bias, RTN is given by the modulation of the trap-assisted current at V+s induced by trapping/detrapping events at O0s, which are, in turn, influenced by the bias itself and by trapped charge at nearby O0s. In this work, we demonstrate that accounting for the effect of trapped charge is essential to provide accurate estimation of the RTN parameters, which allow us to retrieve information about traps and to explain key mechanisms behind complex RTN signals.

Spatially Controlled Generation and Probing of Random Telegraph Noise in Metal Nanocrystal Embedded HfO2 Using Defect Nanospectroscopy

Random telegraph noise (RTN) is often considered a nuisance or, more critically, a key reliability challenge for miniaturized semiconductor devices. However, this picture is gradually changing as recent works have shown emerging applications based on the inherent randomness of the RTN signals in state-of-the-art technologies, including true random number generator and IoT hardware security. Suitable material platforms and device architectures are now actively explored to bring these technologies from an embryonic stage to practical application. A key challenge is to devise material systems, which can be reliably used for the deterministic creation of localized defects to be used for RTN generation. Toward this goal, we have investigated RTN in Au nanocrystal (Au-NC) embedded HfO2 stacks at the nanoscale by combining conduction atomic force microscopy defect spectroscopy and a statistical factorial hidden Markov model analysis. With a voltage applied across the stack, there is an enhanced asymmetric electric field surrounding the Au-NC. This in turn leads to the preferential generation of atomic defects in the HfO2 near the Au-NC when voltage is applied to the stack to induce dielectric breakdown. Since RTN arises from various electrostatic interactions between closely spaced atomic defects, the Au-NC HfO2 material system exhibits an intrinsic ability to generate RTN signals. Our results also highlight that the spatial confinement of multiple defects and the resulting electrostatic interactions between the defects provides a dynamic environment leading to many complex RTN patterns in addition to the presence of the standard two-level RTN signals. The insights obtained at the nanoscale are useful to optimize metal nanocrystal embedded high-κ stacks and circuits for on-demand generation of RTN for emerging random number applications.

SiC Strained nMOSFETs With Enhanced High- Frequency Performance and Impact on Flicker Noise and Random Telegraph Noise

Silicon–carbon (SiC) strained nMOSFETs with sub-35-nm gate length in a 40-nm CMOS technology can realize superior cutoff frequency ( $f_{T}$ ) up to 405 GHz, attributed to more than 20% enhancement of the mobility and transconductance. This super-400-GHz $f_{T}$ makes SiC strained nMOS an attractive high mobility device aimed at millimeter-wave (mm-wave) CMOS circuits design. However, the SiC nMOSFETs reveal a dramatic increase in flicker noise and random telegraph noise (RTN), which may cause worse phase noise and detrimental impact on CMOS oscillator stability. The complex RTN features abnormally long capture and emission time constants ( $\tau _{c}$ and $\tau _{e}$ ) and suggests electron-phonon coupling responsible for the anomalously slow trapping and detrapping, due to a significant increase of relaxation energy from SiC strain. This critical tradeoff between high-frequency performance and low-frequency noise becomes a key factor to be considered for the most appropriate adoption of high mobility devices and design optimization adapted to various circuits.

Chaotic Behavior of Random Telegraph Noise in Nanoscale UTBB FD-SOI MOSFETs

Low frequency noise in nanoscale fully depleted ultra-thin body and buried oxide n-MOSFETs is not as random (stochastic) as it appears to be. The fluctuation of the drain current in such nano-devices, under certain bias conditions, exhibits complex random telegraph noise (RTN) that is usually observed in chaotic systems. Indeed, the deterministic chaotic nature of this noise-like variation is confirmed by means of the combined calculation of established nonlinear dynamics metrics. Specifically, the correlation dimension, the phase portrait of the reconstructed chaotic attractor, and the Lyapunov spectrum, conclude for a hyperchaotic behavior of the complex RTN with fractal characteristics.

A study on HfO2 RRAM in HRS based on I–V and RTN analysis

This paper presents a statistical characterization of random telegraph noise (RTN) in hafnium-oxide-based resistive random access memories (RRAMs) in high resistive state (HRS). Complex RTN signals are analyzed exploiting a Factorial Hidden Markov Model (FHMM) approach, which allows to derive the statistical properties of the RTN signals, directly related to the physical properties of the traps responsible for the multi-level RTN measured in these devices. Noise characteristics in different reset conditions are explored through consecutive switching cycles. Noise spectral analysis is also performed to fully support the investigation. An RRAM compact model is also exploited to estimate the physical properties of the conductive filament and of the dielectric barrier from simple I–V data. These tools are combined together to prove the existence of a direct statistical relation between the reset conditions, the volume of the dielectric barrier created during the reset operation and the average number of active traps contributing to the RTN.