Programming Pulse Width Assessment for Reliable and Low-Energy Endurance Performance in Al:HfO2-Based RRAM Arrays

A crucial step in order to achieve fast and low-energy switching operations in resistive random access memory (RRAM) memories is the reduction of the programming pulse width. In this study, the incremental step pulse with verify algorithm (ISPVA) was implemented by using different pulse widths between 10 μ s and 50 ns and assessed on Al-doped HfO 2 4 kbit RRAM memory arrays. The switching stability was assessed by means of an endurance test of 1k cycles. Both conductive levels and voltages needed for switching showed a remarkable good behavior along 1k reset/set cycles regardless the programming pulse width implemented. Nevertheless, the distributions of voltages as well as the amount of energy required to carry out the switching operations were definitely affected by the value of the pulse width. In addition, the data retention was evaluated after the endurance analysis by annealing the RRAM devices at 150 °C along 100 h. Just an almost negligible increase on the rate of degradation of about 1 μ A at the end of the 100 h of annealing was reported between those samples programmed by employing a pulse width of 10 μ s and those employing 50 ns. Finally, an endurance performance of 200k cycles without any degradation was achieved on 128 RRAM devices by using programming pulses of 100 ns width

Thermal and Electrical Parameter Identification of a Proton Exchange Membrane Fuel Cell Using Genetic Algorithm

[EN] Proton Exchange Membrane Fuel Cell (PEMFC) fuel cells is a technology successfully used in the production of energy from hydrogen, allowing the use of hydrogen as an energy vector. It is scalable for stationary and mobile applications. However, the technology demands more research. An important research topic is fault diagnosis and condition monitoring to improve the life and the efficiency and to reduce the operation costs of PEMFC devices. Consequently, there is a need of physical models that allow deep analysis. These models must be accurate enough to represent the PEMFC behavior and to allow the identification of different internal signals of a PEM fuel cell. This work presents a PEM fuel cell model that uses the output temperature in a closed loop, so it can represent the thermal and the electrical behavior. The model is used to represent a Nexa Ballard 1.2 kW fuel cell; therefore, it is necessary to fit the coefficients to represent the real behavior. Five optimization algorithms were tested to fit the model, three of them taken from literature and two proposed in this work. Finally, the model with the identified parameters was validated with real data.This research was funded by COLCIENCIAS (Administrative department of science, technology and innovation of Colombia) scholarship program PDBCEx, COLDOC 586, and the support provided by the Corporacion Universitaria Comfacauca, Popayan-ColombiaAriza-Chacón, HE.; Correcher Salvador, A.; Sánchez-Diaz, C.; Pérez-Navarro, Á.; García Moreno, E. (2018). Thermal and Electrical Parameter Identification of a Proton Exchange Membrane Fuel Cell Using Genetic Algorithm. Energies. 11(8):1-15. https://doi.org/10.3390/en11082099S115118Mehta, V., & Cooper, J. S. (2003). Review and analysis of PEM fuel cell design and manufacturing. Journal of Power Sources, 114(1), 32-53. doi:10.1016/s0378-7753(02)00542-6Wang, Y., Chen, K. S., Mishler, J., Cho, S. C., & Adroher, X. C. (2011). A review of polymer electrolyte membrane fuel cells: Technology, applications, and needs on fundamental research. Applied Energy, 88(4), 981-1007. doi:10.1016/j.apenergy.2010.09.030Amphlett, J. C., Baumert, R. M., Mann, R. F., Peppley, B. A., Roberge, P. R., & Harris, T. J. (1995). Performance Modeling of the Ballard Mark IV Solid Polymer Electrolyte Fuel Cell: I . Mechanistic Model Development. Journal of The Electrochemical Society, 142(1), 1-8. doi:10.1149/1.2043866Tao, S., Si-jia, Y., Guang-yi, C., & Xin-jian, Z. (2005). Modelling and control PEMFC using fuzzy neural networks. Journal of Zhejiang University-SCIENCE A, 6(10), 1084-1089. doi:10.1631/jzus.2005.a1084Amphlett, J. C., Mann, R. F., Peppley, B. A., Roberge, P. R., & Rodrigues, A. (1996). A model predicting transient responses of proton exchange membrane fuel cells. Journal of Power Sources, 61(1-2), 183-188. doi:10.1016/s0378-7753(96)02360-9Mo, Z.-J., Zhu, X.-J., Wei, L.-Y., & Cao, G.-Y. (2006). Parameter optimization for a PEMFC model with a hybrid genetic algorithm. International Journal of Energy Research, 30(8), 585-597. doi:10.1002/er.1170YE, M., WANG, X., & XU, Y. (2009). Parameter identification for proton exchange membrane fuel cell model using particle swarm optimization. International Journal of Hydrogen Energy, 34(2), 981-989. doi:10.1016/j.ijhydene.2008.11.026Askarzadeh, A., & Rezazadeh, A. (2011). A grouping-based global harmony search algorithm for modeling of proton exchange membrane fuel cell. International Journal of Hydrogen Energy, 36(8), 5047-5053. doi:10.1016/j.ijhydene.2011.01.070El-Fergany, A. A. (2018). Electrical characterisation of proton exchange membrane fuel cells stack using grasshopper optimiser. IET Renewable Power Generation, 12(1), 9-17. doi:10.1049/iet-rpg.2017.0232Li, Q., Chen, W., Wang, Y., Liu, S., & Jia, J. (2011). Parameter Identification for PEM Fuel-Cell Mechanism Model Based on Effective Informed Adaptive Particle Swarm Optimization. IEEE Transactions on Industrial Electronics, 58(6), 2410-2419. doi:10.1109/tie.2010.2060456Ali, M., El-Hameed, M. A., & Farahat, M. A. (2017). Effective parameters’ identification for polymer electrolyte membrane fuel cell models using grey wolf optimizer. Renewable Energy, 111, 455-462. doi:10.1016/j.renene.2017.04.036Sun, Z., Wang, N., Bi, Y., & Srinivasan, D. (2015). Parameter identification of PEMFC model based on hybrid adaptive differential evolution algorithm. Energy, 90, 1334-1341. doi:10.1016/j.energy.2015.06.081Gong, W., Yan, X., Liu, X., & Cai, Z. (2015). Parameter extraction of different fuel cell models with transferred adaptive differential evolution. Energy, 86, 139-151. doi:10.1016/j.energy.2015.03.117Turgut, O. E., & Coban, M. T. (2016). Optimal proton exchange membrane fuel cell modelling based on hybrid Teaching Learning Based Optimization – Differential Evolution algorithm. Ain Shams Engineering Journal, 7(1), 347-360. doi:10.1016/j.asej.2015.05.003Al-Othman, A. K., Ahmed, N. A., Al-Fares, F. S., & AlSharidah, M. E. (2015). Parameter Identification of PEM Fuel Cell Using Quantum-Based Optimization Method. Arabian Journal for Science and Engineering, 40(9), 2619-2628. doi:10.1007/s13369-015-1711-0Methekar, R. N., Prasad, V., & Gudi, R. D. (2007). Dynamic analysis and linear control strategies for proton exchange membrane fuel cell using a distributed parameter model. Journal of Power Sources, 165(1), 152-170. doi:10.1016/j.jpowsour.2006.11.047KUNUSCH, C., HUSAR, A., PULESTON, P., MAYOSKY, M., & MORE, J. (2008). Linear identification and model adjustment of a PEM fuel cell stack. International Journal of Hydrogen Energy, 33(13), 3581-3587. doi:10.1016/j.ijhydene.2008.04.052Li, C.-H., Zhu, X.-J., Cao, G.-Y., Sui, S., & Hu, M.-R. (2008). Identification of the Hammerstein model of a PEMFC stack based on least squares support vector machines. Journal of Power Sources, 175(1), 303-316. doi:10.1016/j.jpowsour.2007.09.049Fontes, G., Turpin, C., & Astier, S. (2010). A Large-Signal and Dynamic Circuit Model of a $\hbox{H}_{2}/\hbox{O}_{2}$ PEM Fuel Cell: Description, Parameter Identification, and Exploitation. IEEE Transactions on Industrial Electronics, 57(6), 1874-1881. doi:10.1109/tie.2010.2044731Cheng, S.-J., & Liu, J.-J. (2015). Nonlinear modeling and identification of proton exchange membrane fuel cell (PEMFC). International Journal of Hydrogen Energy, 40(30), 9452-9461. doi:10.1016/j.ijhydene.2015.05.109Buchholz, M., & Krebs, V. (2007). Dynamic Modelling of a Polymer Electrolyte Membrane Fuel Cell Stack by Nonlinear System Identification. Fuel Cells, 7(5), 392-401. doi:10.1002/fuce.200700013Meiler, M., Schmid, O., Schudy, M., & Hofer, E. P. (2008). Dynamic fuel cell stack model for real-time simulation based on system identification. Journal of Power Sources, 176(2), 523-528. doi:10.1016/j.jpowsour.2007.08.051Wang, C., Nehrir, M. H., & Shaw, S. R. (2005). Dynamic Models and Model Validation for PEM Fuel Cells Using Electrical Circuits. IEEE Transactions on Energy Conversion, 20(2), 442-451. doi:10.1109/tec.2004.842357Restrepo, C., Konjedic, T., Garces, A., Calvente, J., & Giral, R. (2015). Identification of a Proton-Exchange Membrane Fuel Cell’s Model Parameters by Means of an Evolution Strategy. IEEE Transactions on Industrial Informatics, 11(2), 548-559. doi:10.1109/tii.2014.2317982Salim, R., Nabag, M., Noura, H., & Fardoun, A. (2015). The parameter identification of the Nexa 1.2 kW PEMFC’s model using particle swarm optimization. Renewable Energy, 82, 26-34. doi:10.1016/j.renene.2014.10.012Pérez-Navarro, A., Alfonso, D., Ariza, H. E., Cárcel, J., Correcher, A., Escrivá-Escrivá, G., … Vargas, C. (2016). Experimental verification of hybrid renewable systems as feasible energy sources. Renewable Energy, 86, 384-391. doi:10.1016/j.renene.2015.08.03

Performance Assessment of Amorphous HfO2-Based RRAM Devices for Neuromorphic Applications

Producción CientíficaThe use of thin layers of amorphous hafnium oxide has been shown to be suitable for the manufacture of Resistive Random-Access memories (RRAM). These memories are of great interest because of their simple structure and non-volatile character. They are particularly appealing as they are good candidates for substituting flash memories. In this work, the performance of the MIM structure that takes part of a 4 kbit memory array based on 1-transistor-1-resistance (1T1R) cells was studied in terms of control of intermediate states and cycle durability. DC and small signal experiments were carried out in order to fully characterize the devices, which presented excellent multilevel capabilities and resistive-switching behavior.Ministerio de Ciencia, Innovación y Universidades (Grant, TEC2017-84321-C4-2-R )Fondos Feder y la Deutsche Forschungsgemeinschaft (German Research Foundation) ( with Project-ID 434 434 223- SFB1461)The Federal Ministry of Education and Research of Germany under (grant number 16ES1002

Helical surface magnetization in nanowires: the role of chirality

Nanomagnetism is nowadays expanding into three dimensions, triggered by the discovery of new magnetic phenomena and their potential use in applications. This shift towards 3D structures should be accompanied by strategies and methodologies to map the tridimensional spin textures associated. We present here a combination of dichroic X-ray transmission microscopy at different angles and micromagnetic simulations allowing to determine the magnetic configuration of cylindrical nanowires. We have applied it to permalloy nanowires with equispaced chemical barriers that can act as pinning sites for domain walls. The magnetization at the core is longitudinal and generates at the surface of the wire helical magnetization. Different types of domain walls are found at the pinning sites, which respond differently to applied fields depending on the relative chirality of the adjacent domains

Self-growing Colored Petri Net for offshore wind turbines maintenance systems

The offshore wind turbines have been developed in a lot of aspects in the last years, but the big companies are still researching for new techniques that help improve the systems. We propose a new methodology to implement the automatic maintenance system using self-growing colored Petri nets developed in Labview, extendable to other industry systems.Perez Collada, MJ.; Correcher Salvador, A.; García Moreno, E.; Morant Anglada, FJ.; Quiles Cucarella, E. (2011). Self-growing Colored Petri Net for offshore wind turbines maintenance systems. Renewable energy & power quality journal. (9):381-386. http://hdl.handle.net/10251/45123S381386

IFNL3 rs12980275 Polymorphism Predicts Septic Shock-Related Death in Patients Undergoing Major Surgery: A Retrospective Study

Interferon lambda 3 (IFNL3, previously called IL-28B) is a cytokine with effects against viral and bacterial pathogens. We aimed to analyze the IFNL3 rs12980275 SNP in patients who underwent major surgery, in order to establish its relationship with susceptibility to septic shock and septic shock-related death in these patients. We performed a case-control study on 376 patients to establish the association between IFNL3 rs12980275 SNP and the susceptibility to develop septic shock. Besides, we performed a longitudinal study among 172 septic shock patients using survival analysis with one censoring point of 28-days mortality. The IFNL3 rs12980275 polymorphism was genotyped by Agena Bioscience's MassARRAY platform. IFNL3 rs12980275 polymorphism was not associated with higher susceptibility to infection and septic shock development. Regarding survival analysis, the Kaplan-Meier analysis showed that patients with IFNL3 rs12980275 AA genotype had higher survival than patients with GG genotype (p = 0.003). The Cox regression analysis adjusted by the most relevant clinical and epidemiological characteristics showed that the GG genotype (recessive model) and the presence of the G allele (additive model) were associated with higher risk of death [adjusted hazard ratio (aHR) = 2.15, p = 0.034; aHR = 1.50, p = 0.030, respectively]. In conclusion, IFNL3 rs12980275 polymorphism was associated with septic shock-related death in patients who underwent major surgery. The A allele was linked to protection, and the G allele was associated with an increased risk of death. This is a first preliminary study that suggests for the first time a role of IFNL3 polymorphisms in the prognosis of septic shock.This work has been supported by grants given by Instituto de Salud Carlos III (grant numbers PI15/01451 to ET), Gerencia de Salud, Consejería de Sanidad, Junta de Castilla y Leon (grant number GRS 463/A/10 and 773/A/13 to ET), and PFIZER (grant number CT25-ESP01-01 to SR). MJ-S and AF-R are supported by Instituto de Salud Carlos III (grant numbers CP17CIII/00007 and CP14CIII/00010, respectively).S

CEACAM7 polymorphisms predict genetic predisposition to mortality in post-surgical septic shock patients

We carried out a retrospective exploratory study on 173 patients who underwent major surgery and developed septic shock after surgery. Our findings suggest that CEACAM7 rs1001578, rs10409040, and rs889365 polymorphisms could influence septic shock-related death in individuals who underwent major surgery.This work has been supported by grants given by Instituto de Salud Carlos III (grant number PI15/01451 to ET), “Gerencia de Salud, Consejería de Sanidad, Junta de Castilla y Leon” [grant number GRS 463/A/10 and 773/A/13 to ET], and PFIZER [grant number CT25-ESP01-01 to SR]. MAJS and AFR are supported by “Instituto de Salud Carlos III” [grant numbers CP17CIII/00007 and CP14CIII/00010, respectively]S

Neuronal tangential migration from Nkx2.1-positive hypothalamus

During the development of the central nervous system, the immature neurons suffer different migration processes. It is well known that Nkx2.1-positive ventricular layer give rise to critical tangential migrations into different regions of the developing forebrain. Our aim was to study this phenomenon in the hypothalamic region. With this purpose, we used a transgenic mouse line that expresses the tdTomato reporter driven by the promotor of Nkx2.1. Analysing the Nkx2.1-positive derivatives at E18.5, we found neural contributions to the prethalamic region, mainly in the zona incerta and in the mes-diencephalic tegmental region. We studied the developing hypothalamus along the embryonic period. From E10.5 we detected that the Nkx2.1 expression domain was narrower than the reporter distribution. Therefore, the Nkx2.1 expression fades in a great number of the early-born neurons from the Nkx2.1-positive territory. At the most caudal positive part, we detected a thin stream of positive neurons migrating caudally into the mes-diencephalic tegmental region using time-lapse experiments on open neural tube explants. Late in development, we found a second migratory stream into the prethalamic territory. All these tangentially migrated neurons developed a gabaergic phenotype. In summary, we have described the contribution of interneurons from the Nkx2.1-positive hypothalamic territory into two different rostrocaudal territories: the mes-diencephalic reticular formation through a caudal tangential migration and the prethalamic zona incerta complex through a dorsocaudal tangential migratio

Charged-particle multiplicities in pp interactions at root s=900 GeV measured with the ATLAS detector at the LHC

The first measurements from proton-proton collisions recorded with the ATLAS detector at the LHC are presented. Data were collected in December 2009 using a minimum-bias trigger during collisions at a centre-of-mass energy of 900 GeV. The charged-particle multiplicity, its dependence on transverse momentum and pseudorapidity, and the relationship between mean transverse momentum and charged-particle multiplicity are measured for events with at least one charged particle in the kinematic range |eta|500 MeV. The measurements are compared to Monte Carlo models of proton-proton collisions and to results from other experiments at the same centre-of-mass energy. The charged-particle multiplicity per event and unit of pseudorapidity at eta = 0 is measured to be 1.333 +/- 0.003 (stat.) +/- 0.040 (syst.), which is 5-15% higher than the Monte Carlo models predict