An official website of the United States government
The use of thin films made of alloys, i.e., containing multiple metal species, can enhance their properties. However, as with single-element films, residual stress in the films can limit their performance. A model is proposed for relating the stress in alloy thin films to the processing conditions (growth rate, temperature, and sputter-gas pressure), material properties (composition, atomic and defect mobilities, and elastic moduli), and microstructure (grain size and grain growth kinetics). The model is based on stress-generating processes that occur during film growth at grain boundaries and due to energetic particle impacts. While the equations are similar to those proposed for single-element films, the alloy kinetic parameters now contain the effects of the different atomic species. The model is used to explain the growth rate and composition dependence of in situ stress evolution during the deposition for various concentrations in the tungsten–vanadium system.
more »
« less
Phase-field modeling of nanostructural evolution in physical vapor deposited phase-separating ternary alloy films
Abstract Self-assembly by spinodal decomposition is known to be a viable route for synthesizing nanoscaled interfaces in a variety of materials, including metamaterials. In order to tune the response of these specialized materials to external stimuli, knowledge of processing-nanostructure correlations is required. Such an understanding is more challenging to obtain purely by experimental means due to complexity of multicomponent atomic diffusion mechanisms that govern the nanostructural self-assembly. In this work, we introduce a phase-field modeling approach which is capable of simulating the nanostructural evolution in ternary alloy films that are typically synthesized using physical vapor deposition. Based on an extensive parametric study, we analyze the role of the deposition rate and alloy composition on the nanostructural self-assembly in ternary alloy films. The simulated nanostructures are categorized on the basis of nanostructured morphology and mapped over a compositional space to correlate the processing conditions with the film nanostructures. The morphology maps reveal that while deposition rate governs the nanostructural evolution at around equi-molar compositions, the impact of composition on nanostructuring is more pronounced when the atomic ratios of alloying elements are skewed.
more »
« less
- PAR ID:
- 10383507
- Date Published:
- Journal Name:
- Modelling and Simulation in Materials Science and Engineering
- Volume:
- 30
- Issue:
- 8
- ISSN:
- 0965-0393
- Page Range / eLocation ID:
- 084004
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Heterogeneous self-assembly of III–V nanostructures on inert two-dimensional monolayer materials enables novel hybrid nanosystems with unique properties that can be exploited for low-cost and low-weight flexible optoelectronic and nanoelectronic device applications. Here, the pseudo-van der Waals epitaxy (vdWE) growth parameter space for heterogeneous integration of InAs nanowires (NWs) with continuous films of single layer graphene (SLG) via metalorganic chemical vapor deposition (MOCVD) is investigated. The length, diameter, and number density of NWs, as well as areal coverage of parasitic islands, are quantified as functions of key growth variables including growth temperature, V/III ratio, and total flow rate of metalorganic and hydride precursors. A compromise between self-assembly of high aspect ratio NWs comprising high number density arrays and simultaneous minimization of parasitic growth coverage is reached under a selected set of optimal growth conditions. Exploration of NW crystal structures formed under various growth conditions reveals that a characteristic polytypic and disordered lattice is invariant within the explored parameter space. A growth evolution study reveals a gradual reduction in both axial and radial growth rates within the explored timeframe for the optimal growth conditions, which is attributed to a supply-limited competitive growth regime. Two strategies are introduced for further growth optimization. Firstly, it is shown that the absence of a pre-growth in situ arsine surface treatment results in a reduction of parasitic island coverage by factor of ∼0.62, while NW aspect ratio and number densities are simultaneously enhanced. Secondly, the use of a two-step flow-modulated growth procedure allows for realization of dense fields of high aspect ratio InAs NWs. As a result of the applied studies and optimization of the growth parameter space, the highest reported axial growth rate of 840 nm min −1 and NW number density of ∼8.3 × 10 8 cm −2 for vdWE of high aspect ratio (>80) InAs NW arrays on graphitic surfaces are achieved. This work is intended to serve as a guide for vdWE of self-assembled III–V semiconductor NWs such as In-based ternary and quaternary alloys on functional two-dimensional monolayer materials, toward device applications in flexible optoelectronics and tandem-junction photovoltaics.more » « less
-
Nanocomposite thin film materials present great opportunities in coupling materials and functionalities in unique nanostructures including nanoparticles-in-matrix, vertically aligned nanocomposites (VANs), and nanolayers. Interestingly the nanocomposites processed through a non-equilibrium processing method, e.g., pulsed laser deposition (PLD), often possess unique metastable phases and microstructures that could not achieve using equilibrium techniques, and thus lead to novel physical properties. In this work, a unique three-phase system composed of BaTiO3 (BTO), with two immiscible metals, Au and Fe, is demonstrated. By adjusting the deposition laser frequency from 2 Hz to 10 Hz, the phase and morphology of Au and Fe nanoparticles in BTO matrix vary from separated Au and Fe nanoparticles to well-mixed Au-Fe alloy pillars. This is attributed to the non-equilibrium process of PLD and the limited diffusion under high laser frequency (e.g., 10 Hz). The magnetic and optical properties are effectively tuned based on the morphology variation. This work demonstrates the stabilization of non-equilibrium alloy structures in the VAN form and allows for the exploration of new non-equilibrium materials systems and their properties that could not be easily achieved through traditional equilibrium methods.more » « less
-
Combinatorial growth is capable of creating a compositional gradient for thin film materials and thus has been adopted to explore composition variation mostly for metallic alloy thin films and some dopant concentrations for ceramic thin films. This study uses a combinatorial pulsed laser deposition method to successfully fabricate two‐phase oxide–oxide vertically aligned nanocomposite (VAN) thin films of La0.7Sr0.3MnO3(LSMO)‐NiO with variable composition across the film area. The LSMO‐NiO compositional gradient across the film alters the two‐phase morphology of the VAN through varying nanopillar size and density. Additionally, the magnetic anisotropy and magnetoresistance properties of the nanocomposite thin films increase with increasing NiO composition. This demonstration of a combinatorial method for VAN growth can increase the efficiency of nanocomposite thin film research by allowing all possible compositions of thin film materials to be explored in a single deposition.more » « less
-
Deep learning approach for chemistry and processing history prediction from materials microstructureAbstract Finding the chemical composition and processing history from a microstructure morphology for heterogeneous materials is desired in many applications. While the simulation methods based on physical concepts such as the phase-field method can predict the spatio-temporal evolution of the materials’ microstructure, they are not efficient techniques for predicting processing and chemistry if a specific morphology is desired. In this study, we propose a framework based on a deep learning approach that enables us to predict the chemistry and processing history just by reading the morphological distribution of one element. As a case study, we used a dataset from spinodal decomposition simulation of Fe–Cr–Co alloy created by the phase-field method. The mixed dataset, which includes both images, i.e., the morphology of Fe distribution, and continuous data, i.e., the Fe minimum and maximum concentration in the microstructures, are used as input data, and the spinodal temperature and initial chemical composition are utilized as the output data to train the proposed deep neural network. The proposed convolutional layers were compared with pretrained EfficientNet convolutional layers as transfer learning in microstructure feature extraction. The results show that the trained shallow network is effective for chemistry prediction. However, accurate prediction of processing temperature requires more complex feature extraction from the morphology of the microstructure. We benchmarked the model predictive accuracy for real alloy systems with a Fe–Cr–Co transmission electron microscopy micrograph. The predicted chemistry and heat treatment temperature were in good agreement with the ground truth.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1088/1361-651X/aca03f
