7 research outputs found
Thermal Characterization of Conductive Filaments in Unipolar Resistive Memories
A methodology to estimate the device temperature in resistive random access memories
(RRAMs) is presented. Unipolar devices, which are known to be highly influenced by thermal effects
in their resistive switching operation, are employed to develop the technique. A 3D RRAM simulator
is used to fit experimental data and obtain the maximum and average temperatures of the conductive
filaments (CFs) that are responsible for the switching behavior. It is found that the experimental
CFs temperature corresponds to the maximum simulated temperatures obtained at the narrowest
sections of the CFs. These temperature values can be used to improve compact models for circuit
simulation purposesConsejería de Conocimiento, Investigación y Universidad, Junta de Andalucía (Spain)FEDER B-TIC-624-UGR20. M.B.GRamón y Cajal RYC2020-030150-
Modeling the variability of Au/ Ti/h BN/Au memris t ive devices
The variability of memristive devices using
multilayer hexagonal boron nitride (h-BN) coupled with Ti
and Au electrodes (i.e., Au/Ti/h-BN/Au) is analyzed in
depth using different numerical techniques. We extract the
reset voltage using three different methods, quantified its
cycle-to-cycle variability, calculated the charge and flux
that allows to minimize the effects of electric noise and the
inherent stochasticity of resistive switching, described the
device variability using time series analyses to assess the
“memory” effect, and employed a circuit breaker simulator
to understand the formation and rupture of the percolation
paths that produce the switching. We conclude that the
cycle-to-cycle variability of the Au/Ti/h-BN/Au devices
presented here is higher than that previously observed in
Au/h-BN/Au devices, and hence they may be useful for
data encryption.Ministry of Science and
Technology of China (2019YFE0124200, 2018YFE0100800)National Natural Science Foundation of China (61874075)Consejería de Conocimiento, Investigación y Universidad, Junta de
Andalucía (Spain) and European Regional Development Fund (ERDF)
under projects A-TIC-117-UGR18, A-FQM-66-UGR20, A-FQM-345-
UGR18, B-TIC-624-UGR20 and IE2017-5414Grant PGC2018-098860-B-I00 supported by MCIU/AEI/FEDERMaria de Maeztu” Excellence Unit IMAG, reference CEX2020-001105-M, funded
by MCIN/AEI/10.13039/501100011033King Abdullah University of Science and Technolog
Variability and power enhancement of current controlled resistive switching devices
characterized using both current and voltage sweeps, with the device resistance and its cycle-to-cycle variability
being analysed in each case. Experimental measurements indicate a clear improvement on resistance states
stability when using current sweeps to induce both set and reset processes. Moreover, it has been found that
using current to induce these transitions is more efficient than using voltage sweeps, as seen when analysing the
device power consumption. The same results are obtained for devices with a Ni top electrode and a bilayer or
pentalayer of HfO2/Al2O3 as dielectric. Finally, kinetic Monte Carlo and compact modelling simulation studies
are performed to shed light on the experimental resultsConsejería de Conocimiento,
Investigaci´on y Universidad, Junta de Andalucía (Spain)FEDER
program for the project B-TIC-624-UGR20Spanish Consejo
Superior de Investigaciones Científicas (CSIC) for the intramural
project 20225AT012Ramón y Cajal
grant No. RYC2020-030150-I
Spiking neural networks based on two-dimensional materials
The development of artificial neural networks using memristors is gaining a lot of interest among technological companies because
it can reduce the computing time and energy consumption. There is still no memristor, made of any material, capable to provide
the ideal figures-of-merit required for the implementation of artificial neural networks, meaning that more research is required.
Here we present the use of multilayer hexagonal boron nitride based memristors to implement spiking neural networks for image
classification. Our study indicates that the recognition accuracy of the network is high, and that can be resilient to device variability
if the number of neurons employed is large enough. There are very few studies that present the use of a two-dimensional material
for the implementation of synapses of different features; in our case, in addition to a study of the synaptic characteristics of our
memristive devices, we deal with complete spiking neural network training and inference processes.Ministry of Science and Technology, China 2018YFE0100800National Natural Science Foundation of China (NSFC) 61874075Collaborative Innovation Centre of Suzhou Nano Science TechnologyPriority Academic Program Development of Jiangsu Higher Education Institutions111 Project from the State Administration of Foreign Experts Affairs of ChinaJunta de AndaluciaEuropean Commission A-TIC-117-UGR18
B-TIC-624-UGR20
IE2017-5414Spanish GovernmentERDF fund RTI2018-098983-B-I00King Abdullah University of Science & Technolog
Thermal Characterization of Conductive Filaments in Unipolar Resistive Memories
A methodology to estimate the device temperature in resistive random access memories (RRAMs) is presented. Unipolar devices, which are known to be highly influenced by thermal effects in their resistive switching operation, are employed to develop the technique. A 3D RRAM simulator is used to fit experimental data and obtain the maximum and average temperatures of the conductive filaments (CFs) that are responsible for the switching behavior. It is found that the experimental CFs temperature corresponds to the maximum simulated temperatures obtained at the narrowest sections of the CFs. These temperature values can be used to improve compact models for circuit simulation purposes
Thermal Characterization of Conductive Filaments in Unipolar Resistive Memories
A methodology to estimate the device temperature in resistive random access memories (RRAMs) is presented. Unipolar devices, which are known to be highly influenced by thermal effects in their resistive switching operation, are employed to develop the technique. A 3D RRAM simulator is used to fit experimental data and obtain the maximum and average temperatures of the conductive filaments (CFs) that are responsible for the switching behavior. It is found that the experimental CFs temperature corresponds to the maximum simulated temperatures obtained at the narrowest sections of the CFs. These temperature values can be used to improve compact models for circuit simulation purposes
Thermal Compact Modeling and Resistive Switching Analysis in Titanium Oxide-Based Memristors
Resistive switching
devices based on the Au/Ti/TiO2/Au
stack were developed. In addition to standard electrical characterization
by means of I–V curves, scanning
thermal microscopy was employed to localize the hot spots on the top
device surface (linked to conductive nanofilaments, CNFs) and perform
in-operando tracking of temperature in such spots. In this way, electrical
and thermal responses can be simultaneously recorded and related to
each other. In a complementary way, a model for device simulation
(based on COMSOL Multiphysics) was implemented in order to link the
measured temperature to simulated device temperature maps. The data
obtained were employed to calculate the thermal resistance to be used
in compact models, such as the Stanford model, for circuit simulation.
The thermal resistance extraction technique presented in this work
is based on electrical and thermal measurements instead of being indirectly
supported by a single fitting of the electrical response (using just I–V curves), as usual. Besides,
the set and reset voltages were calculated from the complete I–V curve resistive switching series
through different automatic numerical methods to assess the device
variability. The series resistance was also obtained from experimental
measurements, whose value is also incorporated into a compact model
enhanced version