Francesco Maria Puglisi

A Compact Model of Program Window in HfO x RRAM Devices for Conductive Filament Characteristics Analysis

This paper presents a physics-based compact model for the program window in HfOx resistive random access memory devices, defined as the ratio of the resistances in high resistance state (HRS) and low resistance state (LRS). This model allows extracting the characteristics of the conductive filament (CF) in HRS. For a given forming current compliance limit, the program window is shown to be correlated to the thickness of the reoxidized portion of the CF in HRS, which can be modulated by the reset voltage amplitude. On the other hand, the statistical distribution of the memory window depends exponentially on the barrier thickness variations that points to the critical role of reset conditions for the performance optimization of RRAM devices.

An Empirical Model for RRAM Resistance in Low- and High-Resistance States

We present a simple empirical expression describing hafnium-based RRAM resistance at different reset voltages and current compliances. The model that we propose describes filament resistance measured at low ( ~ 0.1 V) reading voltage in both low-resistance state (LRS) and high-resistance state (HRS). The proposed description confirms that conduction in LRS is ohmic (after forming with a sufficiently high current compliance) and is consistent with the earlier description of HRS resistance as controlled by a trap-assisted electron transfer via traps in the oxidized portion of the filament. The length of the nonohmic part of the filament is found to be directly proportional to reset voltage. Moreover, low-frequency noise measurements at different reset voltages evidence a tradeoff between HRS resistance and noise in reading conditions.

A Complete Statistical Investigation of RTN in HfO 2 -Based RRAM in High Resistive State

In this paper, we investigate the random telegraph noise (RTN) in hafnium-oxide resistive random access memories in high resistive state (HRS). The current fluctuations are analyzed by decomposing the multilevel RTN signal into two-level RTN traces using a factorial hidden Markov model approach, which allows extracting the properties of the traps originating the RTN. The current fluctuations, statistically analyzed on devices with a different stack reset at different voltages, are attributed to the activation and deactivation of defects in the oxidized tip of the conductive filament, assisting the trap-assisted tunneling transport in HRS. The physical mechanisms responsible for the defect activation are discussed. We find that RTN current fluctuations can be due to either the coulomb interaction between oxygen vacancies (normally assisting the charge transport) and the electron charge trapped at interstitial oxygen defects, or the metastable defect configuration of oxygen vacancies assisting the electron transport in HRS. A consistent microscopic description of the phenomenon is proposed, linking the material properties to the device performance.

A compact model of hafnium-oxide-based resistive random access memory

In this paper, a compact model of hafnium-oxide-based resistive random access memory (RRAM) cell is developed. The proposed model includes the effect of the temperature and cycle-to-cycle stochastic variations affecting the device operations. Simple I-V measurements are used to extract the model parameters. The model accurately reproduces the I-V curves of the switching cycles in different operating conditions.

Random Telegraph Signal noise properties of HfOx RRAM in high resistive state

In this paper we analyze Random Telegraph Signal (RTS) noise in hafnium-based RRAMs. RTS is measured in HRS, showing fast and slow multilevel switching events. RTS characteristics are examined through novel color-coded time-lag plots and Hidden Markov Model (HMM) time-series analyses. Noise is examined at different reset conditions to provide new insights on conduction mechanisms in HRS. Higher reset voltages result in an enhanced complexity in RTS due to a larger number of active traps.

RTN analysis with FHMM as a tool for multi-trap characterization in HfO X RRAM

This paper presents a new technique to analyze the characteristics of multi-level random telegraph noise (RTN) in HfOX RRAM. RTN is characterized by abrupt switching of either the current or the voltage between discrete values as a result of trapping/de-trapping activity while reading the RRAM cell. RTN statistical 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 RTN, 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.

A microscopic physical description of RTN current fluctuations in HfO x RRAM

In this work we explore the microscopic mechanisms responsible for Random Telegraph Noise (RTN) current fluctuations in HfOx Resistive Random Access Memory (RRAM) devices. The statistical properties of the RTN current fluctuations are analyzed in a variety of reading conditions by exploiting the Factorial Hidden Markov Model (FHMM) to decompose the complex RTN traces in a superimposition of two-level fluctuations. We investigate the physical mechanisms that could be responsible for the RTN current fluctuations by considering two options that are the Coulomb blockade effect and the metastable-to-stable transition of defect assisting the Trap-Assisted-Tunneling (TAT) charge transport. Physics-based simulations show that both options allow reproducing the RTN current fluctuations. The electron TAT via oxygen vacancy defects, responsible for the current in High Resistive State (HRS), is significantly altered by the electric field caused by electron trapping at defects (i.e. neutral interstitial oxygen), not directly involved in charge transport. Similarly, the transition of oxygen vacancies into a stable-slow defect configuration (still unidentified in HfOx) can temporarily switch off the current, thus explaining the RTN.

Instability of HfO 2 RRAM devices: Comparing RTN and cycling variability

In this study, we present an extensive statistical characterization of the cycling variability and Random Telegraph Noise (RTN) in the HfO2-based Resistive Random Access Memories (RRAM) cells. Devices with different dielectric stacks are tested under a variety of read (sampling times and read voltage magnitudes) and operational (reset voltages) conditions. A Factorial Hidden Markov Model (FHMM) analysis is employed to reveal the properties of the traps causing multi-level RTN in High Resistive State (HRS), while the I-V data are analyzed through the developed compact model to investigate cycling variability. The activation and deactivation of traps assisting the charge transport through a dielectric barrier in HRS is found to be responsible for the observed RTN while the read current variations can be attributed to the stochastic nature of the filament oxidation process during reset, also leading to a variable number of traps formed in the barrier after each switching cycle. The statistical characterization of RTN and cycling variability, which demonstrates the uncorrelated nature of these phenomena, provides guidelines for scaling and optimization of RRAM device operations and reliability.

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 t...

Temperature Impact on the Reset Operation in HfO 2 RRAM

In this letter, we report about the impact of temperature on the reset operation in HfO2 resistive random access memory (RRAM) devices. Standard I-V dc characterization (voltage sweeps) is exploited to separately assess the different temperature impact on reset and high resistance state (HRS) verify stages in real operating conditions. The temperature dependence of the processes involved in the two stages is obtained by extracting the effective activation energy of the charge transport in HRS verify, and exploiting a compact model for the reset stage. The compact model links I-V dc measurements to the physical properties of the dielectric barrier defining the HRS in the RRAM. A linear relation is found between barrier thickness and reset temperature. Results suggest that reset may be optimized with respect to the operating temperature to improve cycling variability, especially at ultralow reset voltages.