Thermodynamics and kinetics of ceramic/metal interfacial interactions

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Materials Science and Engineering, 2004.Includes bibliographical references (p. 237-248).This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Ceramic/metal interfaces occur in a great number of important applications, such as ceramic/metal composites, microelectronics packaging, ceramic/metal seals, and so forth. Understanding the formation and evolution of such interfaces is therefore essential for the better design and optimization of these technologies. In this thesis, a methodology for the study of the thermochemical interactions at ceramic/metal interfaces, during both their formation and evolution, is proposed. Because of the importance of zirconia-based ceramics in increasingly important applications such as structural composites, thermal barrier coatings and Solid Oxide Fuel Cells, it was decided to illustrate the concepts developed in this thesis through the study of the interactions between zirconias and active metals. Semi-empirical thermodynamic models of all the phases likely to take part in the ceramic/metal interfacial interactions studied were developed. Phase diagram data and thermochemical information were critically assessed and use to adjust the thermodynamic parameters that allowed the description of the Ag-Cu-Ti, Cu-Ti-Zr, Ti-Zr-O, Cu-Ti-O and Cu-Zr-O systems. The thermodynamic models were used to predict the diffusion paths across zirconia/active metal interfaces through metastable phase diagrams calculations. Additionally, equilibrium calculations of activity diagrams were used to understand the complex interfacial reactions occurring during the active metal brazing of zirconia-based ceramics.(cont.) By using simple one-dimensional interdiffusion simulations, it was demonstrated that the base metal in ceramic/metal joints plays an essential role in determine the thermochemical interactions at the ceramic/metal interface during ceramic/metal joining operations. In general it was found that, using all these techniques,it was possible to explain diffusion paths and reaction sequences observed in a great number of zirconia/active-metal systems, both in the solid and in the liquid states. In many cases, the morphology of the reaction layers formed at ceramic/metal interfaces determine their final properties. To address this problem, empirical thermodynamic models of the likely reaction products at zirconia/metal interfaces were coupled to kinetic models using the diffuse-interface formalism to successfully describe the formation and evolution of complex ceramic/metal interfacial structures.by Raymundo Arróyave.Ph.D

On the Interfacial Phase Growth and Vacancy Evolution during Accelerated Electromigration in Cu/Sn/Cu Microjoints

In this work, we integrate different computational tools based on multi-phase-field simulations to account for the evolution of morphologies and crystallographic defects of Cu/Sn/Cu sandwich interconnect structures that are widely used in three dimensional integrated circuits (3DICs). Specifically, this work accounts for diffusion-driven formation and disappearance of multiple intermetallic phases during accelerated electromigration and takes into account the non-equilibrium formation of vacancies due to electromigration. The work compares nucleation, growth, and coalescence of intermetallic layers during transient liquid phase bonding and virtual joint structure evolution subjected to accelerated electromigration conditions at different temperatures. The changes in the rate of dissolution of Cu from intermetallics and the differences in the evolution of intermetallic layers depending on whether they act as cathodes or anodes are accounted for and are compared favorably with experiments. The model considers non-equilibrium evolution of vacancies that form due to differences in couplings between diffusing atoms and electron flows. This work is significant as the point defect evolution in 3DIC solder joints during electromigration has deep implications to the formation and coalescence of voids that ultimately compromise the structural and functional integrity of the joints.NSF Grant No.CMMI-146225

Phase stability in nanoscale material systems: extension from bulk phase diagrams

Phase diagrams of multi-component systems are critical for the development and engineering of material alloys for all technological applications. At nano dimensions, surfaces (and interfaces) play a significant role in changing equilibrium thermodynamics and phase stability. In this work, it is shown that these surfaces at small dimensions affect the relative equilibrium thermodynamics of the different phases. The CALPHAD approach for material surfaces (also termed “nano-CALPHAD”) is employed to investigate these changes in three binary systems by calculating their phase diagrams at nano dimensions and comparing them with their bulk counterparts. The surface energy contribution, which is the dominant factor in causing these changes, is evaluated using the spherical particle approximation. It is first validated with the Au–Si system for which experimental data on phase stability of spherical nano-sized particles is available, and then extended to calculate phase diagrams of similarly sized particles of Ge–Si and Al–Cu. Additionally, the surface energies of the associated compounds are calculated using DFT, and integrated into the thermodynamic model of the respective binary systems. In this work we found changes in miscibilities, reaction compositions of about 5 at%, and solubility temperatures ranging from 100–200 K for particles of sizes 5 nm, indicating the importance of phase equilibrium analysis at nano dimensions

Multivariate Calibration and Experimental Validation of a 3D Finite ElementThermal Model for Laser Powder-Bed Fusion Metal Additive Manufacturing

Metal additive manufacturing (AM) typically suffers from high degrees of variability in the properties/performance of the fabricated parts, particularly due to the lack of understanding and control over the physical mechanisms that govern microstructure formation during fabrication. This paper directly addresses an important problem in metal AM: the determination of the thermal history of the deposited material. Any attempts to link process to microstructure in AM would need to consider the thermal history of the material. In situ monitoring only provides partial information and simulations may be necessary to have a comprehensive understanding of the thermo-physical conditions to which the deposited material is subjected. We address this in the present work through linking thermal models to experiments via a computationally efficient surrogate modeling approach based on multivariate Gaussian processes (MVGPs). The MVGPs are then used to calibrate the free parameters of the multi-physics models against experiments, sidestepping the use of prohibitively expensive Monte Carlo-based calibration. This framework thus makes it possible to efficiently evaluate the impact of varying process parameter inputs on the characteristics of the melt pool during AM. We demonstrate the framework on the calibration of a thermal model for laser powder bed fusion AM of Ti-6Al-4V against experiments carried out over a wide window in the process parameter space. While this work deals with problems related to AM, its applicability is wider as the proposed framework could potentially be used in many other ICME-based problems where it is essential to link expensive computational materials science models to available experimental data.NASA’s Space Technology ResearchGrants Program, Grant No.NNX15AD71G. NSF-DGE-1545403, “NRT-DESE:Data-Enabled Discovery and Design of Energy Materials (D3EM).” NSF-CMMI-153453

Thermodynamic properties of binary HCP solution phases from special quasirandom structures

Three different special quasirandom structures (SQS) of the substitutional hcp $A_{1-x}B_x$ binary random solutions ($x=0.25$, 0.5, and 0.75) are presented. These structures are able to mimic the most important pair and multi-site correlation functions corresponding to perfectly random hcp solutions at those compositions. Due to the relatively small size of the generated structures, they can be used to calculate the properties of random hcp alloys via first-principles methods. The structures are relaxed in order to find their lowest energy configurations at each composition. In some cases, it was found that full relaxation resulted in complete loss of their parental symmetry as hcp so geometry optimizations in which no local relaxations are allowed were also performed. In general, the first-principles results for the seven binary systems (Cd-Mg, Mg-Zr, Al-Mg, Mo-Ru, Hf-Ti, Hf-Zr, and Ti-Zr) show good agreement with both formation enthalpy and lattice parameters measurements from experiments. It is concluded that the SQS's presented in this work can be widely used to study the behavior of random hcp solutions.Comment: 15 pages, 8 figure

Experiment Design Frameworks for Accelerated Discovery of Targeted Materials Across Scales

Over the last decade, there has been a paradigm shift away from labor-intensive and time-consuming materials discovery methods, and materials exploration through informatics approaches is gaining traction at present. Current approaches are typically centered around the idea of achieving this exploration through high-throughput (HT) experimentation/computation. Such approaches, however, do not account for the practicalities of resource constraints which eventually result in bottlenecks at various stage of the workflow. Regardless of how many bottlenecks are eliminated, the fact that ultimately a human must make decisions about what to do with the acquired information implies that HT frameworks face hard limits that will be extremely difficult to overcome. Recently, this problem has been addressed by framing the materials discovery process as an optimal experiment design problem. In this article, we discuss the need for optimal experiment design, the challenges in it's implementation and finally discuss some successful examples of materials discovery via experiment design

Beyond High throughput: Optimal Computational Materials Discovery

Ciclo de Conferencias "Embajadores" del CENIMThe goal-oriented discovery of materials requires the identification of the composition and process history necessary to achieve specific multi-scale structural features that in turn bring about the desired properties. To accelerate the materials discovery process, high-throughput (HT) computational and experimental methods have been proposed. Unfortunately, current HT computational and experimental approaches have severe limitations as they: (1) are incapable of dealing with the high dimensionality (composition, configurational, and microstructural degrees of freedom) and complexity (e.g., multi-physics) of the materials design space; (2) employ hardcoded workflows and lack flexibility to iteratively learn and adapt based on the knowledge acquired to assure balanced exploration and exploitation of the materials design space; (4) are suboptimal in resource allocation as experimental decisions do not account for the cost and time of experimentation. In this talk, we present some preliminary work in which we have adapted ideas from fields as diverse as Artificial Intelligence, Optimal Experimental Design, Global Optimization and Game Theory to develop a framework capable of optimally exploring the materials design space in order to attain an optimal materials response. Specifically, we use variants of the Efficient Global Optimization algorithm to deploy an autonomous computational materials discovery platform capable of performing optimal sequential computational experiments in order to find optimal materials. We demonstrate single and multi-objective optimization and use the so-called MAX phases as examples. Moreover, we show how this framework can be made robust against selection of non-informative features by using so-called Bayesian Model Averaging approaches.N

Microstructure-Based Modeling of the Effect of Inclusion on the Bendability of Advanced High Strength Dual-Phase Steels

Advanced high strength dual-phase steels are one of the most widely sought-after structural materials for automotive applications. These high strength steels, however, are prone to fracture under bending-dominated manufacturing processes. Experimental observations suggest that the bendability of these steels is sensitive to the presence of subsurface non-metallic inclusions and the inclusions exhibit a rather discrete size effect on the bendability of these steels. Following this, we have carried out a series of microstructure-based finite element calculations of ductile fracture in an advanced high strength dual-phase steel under bending. In the calculations, both the dual-phase microstructure and inclusion are discretely modeled. To gain additional insight, we have also analyzed the effect of an inclusion on the bendability of a single-phase material. In line with the experimental observations, strong inclusion size effect on the bendability of the dual-phase steel naturally emerge in the calculations. Furthermore, supervised machine learning is used to quantify the effects of the multivariable input space associated with the dual-phase microstructure and inclusion on the bendability of the steel. The results of the supervised machine learning are then used to identify the contributions of individual features and isolate critical features that control the bendability of dual-phase steels