1,720,976 research outputs found
Analytical modeling of chalcogenide crystallization for PCM data-retention extrapolation
No full textChalcogenide materials are receiving increasing interest for their many applications as active
materials in emerging memories, such as phase-change memories, programmable metallization
cells, and cross-point devices. The great advantage of these materials is the capability to appear in
two different phases, the amorphous and the crystalline phases, with rather different electrical
properties. The aim of this work is to provide a physically based model for conduction in the
amorphous chalcogenide material, able to predict the current-voltage I−V characteristics as a
function of phase state, temperature, and cell geometry. First, the trap-limited transport at relatively
low currents subthreshold regime is studied, leading to a comprehensive model for subthreshold
conduction accounting for a the shape of the I−V characteristics, b the measured temperature
dependence, c the dependence of subthreshold slope on the thickness of the amorphous phase, and
d the voltage dependence of the activation energy. The threshold switching mechanism is then
explained by the nonequilibrium population in high-mobility shallow traps at high electric field and
by the nonuniform field distribution along the amorphous layer thickness. A single analytical model
is then shown which is able to account for subthreshold conduction, threshold switching, negative
differential resistance region, and ON regime. The model can be applied for fast yet physically
based computation of the current in chalcogenide-based devices e.g., phase change memory cells
and arrays as a function of applied voltage, temperature, and programmed state
Intrinsic data retenction in nanoscaled phase-change memories - Part II: Statistical analysis and prediction of failure time
No full textThe statistical spread of intrinsic data retention times
in phase-change memory (PCM) cells is studied. Based on the
crystallization and percolation model described in Part 1, the
crystalline grain size in the amorphous volume after data loss is
extracted. From the temperature dependence of grain size, the
authors calculate the statistical shape factor for the distribution
of failure times, allowing a statistical prediction of data retention
in PCM large arrays. The scaling and optimization issues with
respect to failure time statistical spread are finally addressed
Modeling and simulation of conduction characteristics and programming operation in nanoscaled phase-change memory cells
No full textIntrinsic data retention in nanoscaled phase-change memories - Part I: Monte Carlo model for crystallization and percolation
No full textThe amorphous phase of chalcogenide material in
phase-change memories (PCMs) is subjected to spontaneous and
thermal-activated crystallization. This represents a critical reliability
issue and has to be carefully investigated and modeled for
physically based projection of retention failure up to ten years. A
new three-dimensional percolation model describing the statistical
crystallization behavior in an intrinsic PCM cell for the amorphous
state is developed. With this physical model, the authors
were able to calculate the resistance evolution with time in the
cell and the statistical distribution of retention failure times in
a cell array. From the impact of geometrical parameters on the
cell retention performance, PCM design guidelines to minimize
data-loss effects can be obtained. The model allows the evaluation
of nucleation and growth parameters and statistical extrapolations
of intrinsic retention failure, which will be shown in Part 2
Filament conduction and reset mechanism in NiO-based resistive-switching memory (RRAM) devices
No full textThe physical understanding of the programming and
reliability mechanisms in resistive-switching memory devices requires
a detailed characterization of the electrical and thermal
conduction properties in the low-resistance state of the memory
cell. The aim of this paper is the characterization of the conductive
filament (CF), which controls the localized current flow in the low
resistive state of the cell. Based on a new technique for evaluating
the CF temperature during operation, we perform a statistical
characterization of the critical filament temperature for the reset
operation, i.e., the transition to the high-resistance state by the
thermal dissolution of the CF. The thermal resistance of the CF
and the activation energy for the dissolution mechanism are then
evaluated, allowing for a physics-based numerical modeling of the
reset operation based on CF thermal breakup
Self-Accelerated Thermal Dissolution Model for Reset Programming in Unipolar Resistive-Switching Memory (RRAM) Devices.
No full textThis paper addresses the numerical modeling of reset programming in NiO-based resistive-switching memory. In our model, we simulate electrical conduction and heating in the conductive filament (CF), which controls the resistance of the low resistive (or set) state, accounting for CF thermal-activated dissolution. Employing CF electrical and thermal parameters, which were previously characterized on our NiO-based samples, our calculations are shown to match experimental reset and retention characteristics. Simulations show that reset transition is self-accelerated as a consequence of a positive feedback between the thermal dissolution of the CF and local Joule heating in the CF bottleneck, which can account for the abrupt resistance transition in experimental data. Finally, the model is used to investigate the reduction of the reset current, which is needed for device application
Modeling of programming and read performance in phase-change memories - Part I: cell optimization and scaling
No full textOne of the major concerns for the feasibility of phase-change memories is the reduction of the programming current. To this aim, several efforts have been dedicated both on cell geometry and on material engineering. This paper addresses programming-current minimization by the optimization of the cell geometry and materials, programming-current scaling, and the tradeoff between programming and readout performances of the cell. A general procedure to find the optimum-cell geometry is proposed and applied to a prototype vertical cell. Then, the evolution of program and read performances through technology nodes is analyzed by numerical simulations with the aid of an analytical model, for both the isotropic- and nonisotropic-scaling approaches. The two scaling approaches are discussed and compared in terms of program and read cell performances. Finally, material optimization is considered for further program-read improvement
Voltage-driven ON-OFF transition and tradeoff with program and erase current in programmable metallization cell (PMC) memory
No full text
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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