Síntesis y evaluación farmacológica de nuevas moléculas multipotentes para el tratamiento de la enfermedad de Alzheimer

Tesis doctoral inédita leída en la Universidad Autónoma de Madrid, Faculta de Ciencias, Departamento de Química Orgánica. Fecha de lectura: 28-11-201

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

Holistic Variability Analysis in Resistive Switching Memories Using a Two-Dimensional Variability Coefficient

The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acsami.2c22617We present a new methodology to quantify the variability of resistive switching memories. Instead of statistically analyzing few data points extracted from current versus voltage (I− V) plots, such as switching voltages or state resistances, we take into account the whole I−V curve measured in each RS cycle. This means going from a one-dimensional data set to a two-dimensional data set, in which every point of each I−V curve measured is included in the variability calculation. We introduce a new coefficient (named two-dimensional variability coefficient, 2DVC) that reveals additional variability information to which traditional one-dimensional analytical methods (such as the coefficient of variation) are blind. This novel approach provides a holistic variability metric for a better understanding of the functioning of resistive switching memoriesConsejería de Conocimiento, Investigación y Universidad, Junta de Andalucía (Spain)FEDER: B-TIC-624-UGR20, PID2020-113961GB-I00, A-FQM-66-UGR20, FQM-307IMAG María de Maeztu CEX2020-001105-M/AEI/10.13039/501100011033King Abdullah University of Science and Technolog

N-methyl-N-((1-methyl-5-(3-(1-(2-methylbenzyl)piperidin-4-yl)propoxy)-1H-indol-2-yl)methyl)prop-2-yn-1-amine, a new cholinesterase and monoamine oxidase dual inhibitor

On the basis of N-((5-(3-(1-benzylpiperidin-4-yl)propoxy)-1-methyl-1H-indol-2-yl)methyl)-N-methylprop-2-yn-1-amine (II, ASS234) and QSAR predictions, in this work we have designed, synthesized, and evaluated a number of new indole derivatives from which we have identified N-methyl-N-((1-methyl-5-(3-(1-(2-methylbenzyl)piperidin-4-yl)propoxy)-1H-indol-2-yl)methyl)prop-2-yn-1-amine (2, MBA236) as a new cholinesterase and monoamine oxidase dual inhibitor.PostprintPostprintPeer reviewe

A Complex Model via Phase-Type Distributions to Study Random Telegraph Noise in Resistive Memories

A new stochastic process was developed by considering the internal performance of macro-states in which the sojourn time in each one is phase-type distributed depending on time. The stationary distribution was calculated through matrix-algorithmic methods and multiple interesting measures were worked out. The number of visits distribution to a determine macro-state were analyzed from the respective differential equations and the Laplace transform. The mean number of visits to a macro-state between any two times was given. The results were implemented computationally and were successfully applied to study random telegraph noise (RTN) in resistive memories. RTN is an important concern in resistive random access memory (RRAM) operation. On one hand, it could limit some of the technological applications of these devices; on the other hand, RTN can be used for the physical characterization. Therefore, an in-depth statistical analysis to model the behavior of these devices is of essential importance.Spanish Ministry of Science, Innovation and Universities (FEDER program) MTM2017-88708-P TEC2017-84321-C4-3-RGovernment of Andalusia (Spain) FQM-307Andalusian Ministry of Economy, Knowledge, Companies and Universities A-TIC-117-UGR18 FPU18/0177

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

Open Science Assessment and Incentives at the YUFE Alliance

Proceeding of: 26th International Conference on Science and Technology Indicators, STI 2022. September 7-9, 2022. GranadaThe paper has been written as part of the YUFERING project, which is a three-year project by the YUFE university alliance. The project has received funding from the Horizon 2020 programme (GA: 101016967)