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In this paper, the nonlinear switching mechanism of the Ti/HfO2/Al2O3/TiN crossbar structure resistive random access memory device with good reliability is investigated. The nonlinearity of the device can be revealed by inserting a large bandgap of an Al2O3 thin layer between the TiN bottom electrode and the HfO2 switching film. The nonlinear switching mechanism caused by Flower–Nordheim tunneling involves the tunneling barrier of the Al2O3 layer. Besides, the nonlinear behavior is also sensitive to the thickness of the inserting Al2O3 layer. A high nonlinear factor of 37, large endurance more than 10^4 , and good retention properties are achieved in the Ti/HfO2/Al2O3 (1-nm)/TiN structure. [ABSTRACT FROM AUTHOR]

In this paper, the use of HfO2-based oxide-based resistive memory (OxRAM) devices operated in binary mode to implement synapses in a convolutional neural network (CNN) is studied. We employed an artificial synapse composed of multiple OxRAM cells connected in parallel, thereby providing synaptic efficacies. Electrical characterization results show that the proposed HfO2-based OxRAM technology offers good electrical properties in terms of endurance ( > 10^8 cycles), speed (<10 ns), and low energy (<10 pJ), and thus being well suited for neuromorphic applications. A device physical model is developed in order to study the variability of the resistance as a function of the stochastic position of oxygen vacancies in 3-D. Finally, the proposed binary OxRAM synapse has been used for CNN system-level simulations. High accuracy (recognition rate > 98%) is demonstrated for a complex visual pattern recognition application. We demonstrated that the resistance variability and the reduced memory window of the OxRAM cells when operated at extremely low programming conditions (<10 pJ per switching event) have a small impact on the performances of proposed OxRAM-based CNN (recognition rate 94%). [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

A model of resistive random access memory (RRAM) cells aimed at providing an optimal programming/erase scheme in which the timing and biasing can be accurately optimized is proposed and implemented. To expedite technology development with an emphasis on ICs, a predictive model to capture the physical operation of every memory cell is needed. Although a number of compact RRAM models have been developed, this paper further considers the time-dependent reset process and the heat transfer in the conductive filaments. These phenomena are becoming critical in scaled memory cells and need to be carefully addressed. Due to the physical nature of the model, model parameters can be straightforwardly calibrated, relying on limited measurement data. The compact model is implemented using Verilog-A, and it is flexible for different circuit simulators. [ABSTRACT FROM PUBLISHER]

Resistive switching memory (RRAM) relies on the voltage-driven formation/disruption of a conductive filament (CF) across a thin insulating layer. Due to the 1-D structure of the CF and discrete nature of defects, the set and reset states of the memory device generally display statistical variability from cycle to cycle. For projecting cell downscaling and designing improved programming operations, the variability as a function of the operation parameters, such as the maximum current in the set process and maximum voltage in the reset process, need to be evaluated and understood. This paper addresses set/reset variability, presenting statistical data for HfOx-based RRAM and introducing a physics-based Monte Carlo model for switching statistics. The model can predict the distribution of the set state as a function of the compliance (maximum) current during set and distribution of the reset state as a function of the stop (maximum) voltage during reset. Numerical modeling results are finally presented to provide additional insight into discrete fluctuation events. [ABSTRACT FROM AUTHOR]

Resistive-switching random access memory (RRAM) devices utilizing a crossbar architecture represent a promising alternative for Flash replacement in high-density data storage applications. However, RRAM crossbar arrays require the adoption of diodelike select devices with high on–off -current ratio and with sufficient endurance. To avoid the use of select devices, one should develop passive arrays where the nonlinear characteristic of the RRAM device itself provides self-selection during read and write. This paper discusses the complementary switching (CS) in hafnium oxide RRAM, where the logic bit can be encoded in two high-resistance levels, thus being immune from leakage currents and related sneak-through effects in the crossbar array. The CS physical mechanism is described through simulation results by an ion-migration model for bipolar switching. Results from pulsed-regime characterization are shown, demonstrating that CS can be operated at least in the 10-ns time scale. The minimization of the reset current is finally discussed. [ABSTRACT FROM AUTHOR]

In this paper, we study the effect of highly doped n^+/p^+ Si as the bottom electrode on unipolar RRAM with Ni-electrode/\HfOx structure. With heavily doped p^+-Si as the bottom electrode, RRAM devices illustrate the coexistence of the bipolar and the unipolar resistive switching. Meanwhile, by substituting heavily doped n^+-Si, the switching behavior changes to that of the self-rectifying unipolar device. The asymmetry and rectifying reproducible behavior in a n^+-\Si/HfOx/\Ni device resulted from the Schottky barrier of defect states in the \SiOx/\HfOx junction and n^+ Si substrate, but this behavior is not seen for the p^+-Si bottom electrode case. With rectifying characteristics and high forward current density observed in the \Ni/HfOx/\n^{+}\Si device, the sneak current path in the conventional crossbar architecture was significantly suppressed. We believe that the proposed structure is a promising candidate for future crossbar-type RRAM applications. [ABSTRACT FROM AUTHOR]