Tunnelling effects in multiferroic tunnel junctions

The demands from electronic devices have always been to be portable, fast, non-volatile, more intelligent and to consume low energy. One way towards this goal is to introduce multifunctionality of materials in devices. Ferromagnetism and ferroelectricity are two order parameters that can be coupled in a limited number of multiferroics and their coexistence implies the control over magnetisation and polarisation with both electric and magnetic fields. Similar properties were observed at ferromagnetic/ferroelectric thin film interfaces and attracted attention, since high quality thin film devices can be easily obtained nowadays through monitoring in real time of their structural and physical properties. This effect was observed also in tunnel junction configurations, devices which are formed from metallic electrodes separated by a very thin insulating barrier. By combining a barrier with various ferroelectric order parameters (ferroelectric, antiferroelectric, ferrielectric) and ferromagnetic electrodes, multi-field controlled multi-state non-volatile memory devices can be obtained. Tunnelling processes, especially in junctions containing d orbital elements are not completely understood and need deeper investigation. In this thesis, multiferroic tunnel junctions with La0:7Sr0:3MnO3/PbTiO3/Co structure are shown to be functional down to 3 unit cells. Moreover, the domain structure is shown to change with thickness, going through complex patterns including toroidal flux closure structures. The fabrication and working principle of the novel antiferroelectric tunnel junctions are reported for the first time using La0:7Sr0:3MnO3/PbZrO3/Co structures. Both investigated systems exhibit a multiferroic interface characterised by a magnetoelectric coupling which can be tailored by switching the ferroelectric polarisation

Polarization curling and flux closures in multiferroic tunnel junctions

Formation of domain walls in ferroelectrics is not energetically favourable in low-dimensional systems. Instead, vortex-type structures are formed that are driven by depolarization fields occurring in such systems. Consequently, polarization vortices have only been experimentally found in systems in which these fields are deliberately maximized, that is, in films between insulating layers. As such configurations are devoid of screening charges provided by metal electrodes, commonly used in electronic devices, it is wise to investigate if curling polarization structures are innate to ferroelectricity or induced by the absence of electrodes. Here we show that in unpoled Co/PbTiO3/(La,Sr)MnO3 ferroelectric tunnel junctions, the polarization in active PbTiO3 layers 9 unit cells thick forms Kittel-like domains, while at 6 unit cells there is a complex flux-closure curling behaviour resembling an incommensurate phase. Reducing the thickness to 3 unit cells, there is an almost complete loss of switchable polarization associated with an internal gradient

Jointly learning consistent causal abstractions over multiple interventional distributions

An abstraction can be used to relate two structural causal models representing the same system at different levels of resolution. Learning abstractions which guarantee consistency with respect to interventional distributions would allow one to jointly reason about evidence across multiple levels of granularity while respecting the underlying cause-effect relationships. In this paper, we introduce a first framework for causal abstraction learning between SCMs based on the formalization of abstraction recently proposed by Rischel (2020). Based on that, we propose a differentiable programming solution that jointly solves a number of combinatorial sub-problems, and we study its performance and benefits against independent and sequential approaches on synthetic settings and on a challenging real-world problem related to electric vehicle battery manufacturing

Machine learning for investigating the relative importance of electrodes’ N:P areal capacity ratio in the manufacturing of lithium-ion battery cells

This work studies the impact of the ratio between the areal capacity of Graphite anode to NMC622 cathode for Lithium-ion batteries compared to the electrode characteristics of thickness, mass loading and cathode areal capacity, on their electrochemical properties. The influence of factors on energy capacity and gravimetric capacity at various Crates starting from C/20 up to 10C is quantified by combining experiments obtained via design of experiment techniques, machine learning modelling and explanation techniques. The results highlight that the performance at all Crates is highly affected by all features however their relative importance, and the linearity and nonlinearity of the dependencies is quite unique for each Crate capacity. N:P ratio is showing a relatively smaller effect on electrochemical performance compared to thickness, mass loading of active material and cathode areal capacity. It is also concluded that while the impact of N:P ratio is almost linear at lower Crates, it is nonlinear with a local optimum at medium and high Crates. This study offers a methodology for smart selection of a ratio between anode and cathode aerial capacity for a balanced performance of cells at all Crates

Bi-ferroic memristive properties of multiferroic tunnel junctions

The giant tunnelling electroresistance (TER) and memristive behaviours of ferroelectric tunnel junctions make them promising candidates for future information storage technology. Using conducting ferromagnetic layers as electrodes results in multiferroic tunnel junctions (MFTJs) which show spin dependent transport. The tunnelling magnetoresistance (TMR) of such structures can be reversibly controlled by electric pulsing owing to ferroelectric polarisation-dependent spin polarisation at the ferroelectric/ferromagnetic interface. Here, we show multilevel electric control of both TMR and TER of MFTJs, which indicates the bi-ferroic or magneto-electric memristive properties. This effect is realised by manipulating the ferroelectric domain configuration via non-volatile partial ferroelectric switching obtained by applying low voltage pulses to the junction. Through electrically modulating the ratio between up- and down-polarised ferroelectric domains, a broad range of TMR (between ∼3% and ∼30%) and TER (∼1000%) values can be achieved. The multilevel control of TMR and TER using the electric pulse tunable ferroelectric domain configuration suggests a viable way to obtain multiple state memory

Cross-sectional analysis of lithium ion electrodes using spatial autocorrelation techniques

Join counting, a standard technique in spatial autocorrelation analysis, has been used to quantify the clustering of carbon, fluorine and sodium in cross-sectioned anode and cathode samples. The sample preparation and EDS mapping steps are sufficiently fast for every coating from two Design of Experiment (DoE) test matrices to be characterised. The results show two types of heterogeneity in material distribution; gradients across the coating from the current collector to the surface, and clustering. In the cathode samples, the carbon is more clustered than the fluorine, implying that the conductive carbon component is less well distributed than the binder. The results are correlated with input parameters systematically varied in the DoE e.g. coating blade gap, coating speed, and other output parameters e.g. coat weight, and electrochemical resistance

Interpretable machine learning for battery capacities prediction and coating parameters analysis

Battery manufacturing plays a direct and pivotal role in determining battery performance, which, in turn, significantly affects the applications of battery-related energy storage systems. As a complicated process that involves chemical, mechanical and electrical operations, effective battery property predictions and reliable analysis of strongly-coupled battery manufacturing parameters or variables become the key but challenging issues for wider battery applications. In this paper, an interpretable machine learning framework that could effectively predict battery product properties and explain dynamic effects, as well as interactions of manufacturing parameters is proposed. Due to the data-driven nature, this framework can be easily adopted by engineers as no specific battery manufacturing mechanism knowledge is required. Reliable battery manufacturing dataset particularly for coating (one key stage) collected from a real battery manufacturing chain is adopted to evaluate the proposed framework. Illustrative results demonstrate that three types of battery capacities including cell capacity, gravimetric capacity, and volumetric capacity can be accurately predicted with over 0.98 at the battery early-manufacturing stage. Besides, information regarding how the variations of coating mass, thickness, and porosity affect these battery capacities is effectively identified, while interactions of these coating parameters can be also quantified. The developed framework makes the data-driven model become more interpretable and opens a promising way to quantify the interactions of battery manufacturing parameters and explain how the variations of these parameters affect final battery properties. This could assist engineers to obtain critical insights to understand the underlying complicated battery material and manufacturing behavior, further benefiting smart control of battery manufacturing

Quantifying key factors for optimised manufacturing of Li-ion battery anode and cathode via artificial intelligence

Li-ion battery is one of the key players in energy storage technology empowering electrified and clean transportation systems. However, it is still associated with high costs due to the expensive material as well as high fluctuations of the manufacturing process. Complicated production processes involving mechanical, chemical, and electrical operations makes the predictability of the manufacturing process a challenge, hence the process is optimised through trial and error rather systematic simulation. To establish an in-depth understanding of the interconnected processes and manufacturing parameters, this paper combines data-mining techniques and real production to offer a method for the systematic analysis, understanding and improving the Li-ion battery electrode manufacturing chain. The novelty of this research is that unlike most of the existing research that are focused on cathode manufacturing only, it covers both of the cathode and anode case studies. Furthermore, it is based on real manufacturing data, proposes a systematic design of experiment method for generating high quality and representative data, and leverages the artificial intelligence techniques to identify the dependencies in between the manufacturing parameters and the key quality factors of the electrode. Through this study, machine learning models are developed to quantify the predictability of electrode and cell properties given the coating process control parameters. Moreover, the manufacturing parameters are ranked and their contribution to the electrode and cell characteristics are quantified by models. The systematic data acquisition approach as well as the quantified interdependencies are expected to assist the manufacturer when moving towards an improved battery production chain

In-plane tunnelling field-effect transistor integrated on Silicon

Altres ajuts: Beatriu de Pinós postdoctoral scholarship (2011 BP-A 00220 and 2011 BP-A_2 00014) from AGAUR-Generalitat de CatalunyaSilicon has persevered as the primary substrate of microelectronics during last decades. During last years, it has been gradually embracing the integration of ferroelectricity and ferromagnetism. The successful incorporation of these two functionalities to silicon has delivered the desired non-volatility via charge-effects and giant magneto-resistance. On the other hand, there has been a numerous demonstrations of the so-called magnetoelectric effect (coupling between ferroelectric and ferromagnetic order) using nearly-perfect heterostructures. However, the scrutiny of the ingredients that lead to magnetoelectric coupling, namely magnetic moment and a conducting channel, does not necessarily require structural perfection. In this work, we circumvent the stringent requirements for epilayers while preserving the magnetoelectric functionality in a silicon-integrated device. Additionally, we have identified an in-plane tunnelling mechanism which responds to a vertical electric field. This genuine electroresistance effect is distinct from known resistive-switching or tunnel electro resistance

Optimisation of industrially relevant electrode formulations for LFP cathodes in lithium ion cells

The electrode formulation has a significant effect on the performance of lithium ion cells. The active material, binder, and conductive carbon all have different roles, and finding the optimum composition can be difficult using an iterative approach. In this study, a design of experiment (DoE) methodology is applied to the optimisation of a cathode based on lithium iron phosphate (LFP). The minimum LFP content in the electrodes is 94 wt%. Seventeen mixes are used to evaluate adhesion, resistivity, and electrochemical performance. The coating adhesion increases with binder content, and the coating conductivity increases with carbon nano-tube content. The best coatings achieve 5C:0.2C capacity ratios above 50%, despite the relatively high coat weight. Models based on just the component mixture do not replicate the discharge capacities at high rates. However, a combined mixture + process model can fit the data, and is used to predict an optimum formulation