Integrating Materials and Manufacturing Innovation

Sputter-Deposited Mo Thin Films: Characterization of Grain Structure and Monte Carlo Simulations of Sputtered Atom Energies and Incidence Angles

Joyce O. Custer
M Kalaswad
R. S. Kothari
P. G. Kotula
T. Ruggles
A. Hinojos
R Dingreville
A. Henriksen
D. P. Adams
Show full list: 9 authors
Publication typeJournal Article
Publication date2024-12-12
scimago Q1
wos Q3
SJR0.742
CiteScore5.3
Impact factor2.4
ISSN21939764, 21939772
Shrivastava A., Kalaswad M., Custer J.O., Adams D.P., Najm H.N.
2024-04-24 citations by CoLab: 1 Abstract  
We introduce a Bayesian optimization approach to guide the sputter deposition of molybdenum thin films, aiming to achieve desired residual stress and sheet resistance while minimizing susceptibility to stochastic fluctuations during deposition. Thin films are pivotal in numerous technologies, including semiconductors and optical devices, where their properties are critical. Sputter deposition parameters, such as deposition power, vacuum chamber pressure, and working distance, influence physical properties like residual stress and resistance. Excessive stress and high resistance can impair device performance, necessitating the selection of optimal process parameters. Furthermore, these parameters should ensure the consistency and reliability of thin film properties, assisting in the reproducibility of the devices. However, exploring the multidimensional design space for process optimization is expensive. Bayesian optimization is ideal for optimizing inputs/parameters of general black-box functions without reliance on gradient information. We utilize Bayesian optimization to optimize deposition power and pressure using a custom-built objective function incorporating observed stress and resistance data. Additionally, we integrate prior knowledge of stress variation with pressure into the objective function to prioritize films least affected by stochastic variations. Our findings demonstrate that Bayesian optimization effectively explores the design space and identifies optimal parameter combinations meeting desired stress and resistance specifications.
Kalaswad M., Custer J.O., Addamane S., Khan R.M., Jauregui L., Babuska T.F., Henriksen A., DelRio F.W., Dingreville R., Boyce B.L., Adams D.P.
2023-04-24 citations by CoLab: 5 Abstract  
Multimodal datasets of materials are rich sources of information which can be leveraged for expedited discovery of process–structure–property relationships and for designing materials with targeted structures and/or properties. For this data descriptor article, we provide a multimodal dataset of magnetron sputter-deposited molybdenum (Mo) thin films, which are used in a variety of industries including high temperature coatings, photovoltaics, and microelectronics. In this dataset we explored a process space consisting of 27 unique combinations of sputter power and Ar deposition pressure. The phase, structure, surface morphology, and composition of the Mo thin films were characterized by x-ray diffraction, scanning electron microscopy, atomic force microscopy, and Rutherford backscattering spectrometry. Physical properties—namely, thickness, film stress and sheet resistance—were also measured to provide additional film characteristics and behaviors. Additionally, nanoindentation was utilized to obtain mechanical load-displacement data. The entire dataset consists of 2072 measurements including scalar values (e.g., film stress values), 2D linescans (e.g., x-ray diffractograms), and 3D imagery (e.g., atomic force microscopy images). An additional 1889 quantities, including film hardness, modulus, electrical resistivity, density, and surface roughness, were derived from the experimental datasets using traditional methods. Minimal analysis and discussion of the results are provided in this data descriptor article to limit the authors’ preconceived interpretations of the data. Overall, the data modalities are consistent with previous reports of refractory metal thin films, ensuring that a high-quality dataset was generated. The entirety of this data is committed to a public repository in the Materials Data Facility.
Martin T.B., Audus D.J.
2023-01-18 citations by CoLab: 64 PDF Abstract  
In the last five years, there has been tremendous growth in machine learning and artificial intelligence as applied to polymer science. Here, we highlight the unique challenges presented by polymers and how the field is addressing them. We focus on emerging trends with an emphasis on topics that have received less attention in the review literature. Finally, we provide an outlook for the field, outline important growth areas in machine learning and artificial intelligence for polymer science and discuss important advances from the greater material science community.
Bharti K., Haug T., Vedral V., Kwek L.
AVS Quantum Science scimago Q1 wos Q2
2020-07-10 citations by CoLab: 37 Abstract  
The goal of machine learning is to facilitate a computer to execute a specific task without explicit instruction by an external party. Quantum foundations seek to explain the conceptual and mathematical edifice of quantum theory. Recently, ideas from machine learning have successfully been applied to different problems in quantum foundations. Here, the authors compile the representative works done so far at the interface of machine learning and quantum foundations. The authors conclude the survey with potential future directions.
Kusano E.
2019-11-30 citations by CoLab: 38 Abstract  
In this paper, the structure zone model (SZM) for sputter-deposited thin films is reviewed through a systematic discussion of the dependence of film structure and properties on the discharge pressure and homologous substrate temperature. The SZM is applicable to the sputter-deposited metal and metal oxide thin films with a film thickness ranging from a few to several hundred nanometers. Furthermore, the SZM is evolved for the sputter-deposition of films through energization of the sputtered particles by replacing the axis of the deposition pressure with that of the effective energy per depositing atom. The results suggest that the SZM is valid as a conceptual diagram with respect to optimizing film-deposition conditions for industrial applications.
Rickman J.M., Lookman T., Kalinin S.V.
Acta Materialia scimago Q1 wos Q1
2019-04-01 citations by CoLab: 133 Abstract  
In recent years materials informatics, which is the application of data science to problems in materials science and engineering, has emerged as a powerful tool for materials discovery and design. This relatively new field is already having a significant impact on the interpretation of data for a variety of materials systems, including those used in thermoelectrics, ferroelectrics, battery anodes and cathodes, hydrogen storage materials, polymer dielectrics, etc. Its practitioners employ the methods of multivariate statistics and machine learning in conjunction with standard computational tools (e.g., density-functional theory) to, for example, visualize and dimensionally reduce large data sets, identify patterns in hyperspectral data, parse microstructural images of polycrystals, characterize vortex structures in ferroelectrics, design batteries and, in general, establish correlations to extract important physics and infer structure-property-processing relationships. In this Overview, we critically examine the role of informatics in several important materials subfields, highlighting significant contributions to date and identifying known shortcomings. We specifically focus attention on the difference between the correlative approach of classical data science and the causative approach of physical sciences. From this perspective, we also outline some potential opportunities and challenges for informatics in the materials realm in this era of big data.
Hofer-Roblyek A.M., Pichler K., Linke C., Franz R., Winkler J., Mitterer C.
Surface and Coatings Technology scimago Q1 wos Q1
2018-10-01 citations by CoLab: 13 Abstract  
The use of Mo in large area thin film deposition includes back contact layers for thin film solar cells as well as diffusion barriers and source/drain electrodes in microelectronics and relies on its excellent thermal stability and chemical inertness as well as low electrical resistivity. A constant high quality of sputter deposited thin films during the entire target lifetime is of vital importance for these applications. Thus, this study addresses the sputter performance, i.e. changes of current, voltage and arc rate, recorded during erosion of a rotatable Mo target as well as the quality of thin films deposited at different erosion stages. The enhanced target erosion and the thus reduced target wall thickness cause an increase of the magnetic field strength in front of the target and yield a slightly reduced voltage and increased current. Increased arc rates could be related to venting the vacuum chamber during interruptions in target erosion which were needed for thin film depositions. Both, microstructure and electrical resistivity of the films deposited are widely unaffected by the progressing target erosion. In contrast, different substrate carrier oscillation modes determine film topography, stress and electrical resistivity. The end of target life is determined by the pronounced sputter grooves formed at both ends of the rotatable target due to the shape of the permanent magnetic field at the turnarounds rather than changes in the quality of the films deposited.
Ramprasad R., Batra R., Pilania G., Mannodi-Kanakkithodi A., Kim C.
npj Computational Materials scimago Q1 wos Q1 Open Access
2017-12-07 citations by CoLab: 1200 PDF Abstract  
Propelled partly by the Materials Genome Initiative, and partly by the algorithmic developments and the resounding successes of data-driven efforts in other domains, informatics strategies are beginning to take shape within materials science. These approaches lead to surrogate machine learning models that enable rapid predictions based purely on past data rather than by direct experimentation or by computations/simulations in which fundamental equations are explicitly solved. Data-centric informatics methods are becoming useful to determine material properties that are hard to measure or compute using traditional methods—due to the cost, time or effort involved—but for which reliable data either already exists or can be generated for at least a subset of the critical cases. Predictions are typically interpolative, involving fingerprinting a material numerically first, and then following a mapping (established via a learning algorithm) between the fingerprint and the property of interest. Fingerprints, also referred to as “descriptors”, may be of many types and scales, as dictated by the application domain and needs. Predictions may also be extrapolative—extending into new materials spaces—provided prediction uncertainties are properly taken into account. This article attempts to provide an overview of some of the recent successful data-driven “materials informatics” strategies undertaken in the last decade, with particular emphasis on the fingerprint or descriptor choices. The review also identifies some challenges the community is facing and those that should be overcome in the near future.
Jörg T., Cordill M.J., Franz R., Glushko O., Winkler J., Mitterer C.
Thin Solid Films scimago Q2 wos Q3
2016-05-01 citations by CoLab: 46 Abstract  
The electro-mechanical performance of Mo thin films grown by dc magnetron sputter deposition on polyimide substrates was investigated. A series of 500 nm thick Mo films was synthesized at different discharge powers to evaluate the effect of deposition conditions on the structure–stress relationship and to correlate the intrinsic properties of the Mo films with their electro-mechanical response. Different in-situ fragmentation tests were performed to assess the crack morphology, the change in electrical resistance and the film stress during straining. A direct relation between the residual stress state of the Mo thin films and the discharge power was noticed as the stress changed from tensile to compressive with increasing discharge power. All Mo films showed brittle fracture when strained in tension and the critical crack onset strain correlated with the residual stress state. In-situ synchrotron diffraction experiments enabled the characterization of the fracture strength, which was unaltered by the discharge power and found to be approximately 1700 MPa for all Mo thin films studied.
Zhou D., Zhu H., Liang X., Zhang C., Li Z., Xu Y., Chen J., Zhang L., Mai Y.
Applied Surface Science scimago Q1 wos Q1
2016-01-01 citations by CoLab: 54 Abstract  
Molybdenum (Mo) thin films are prepared by magnetron sputtering with different discharge powers and working pressures for the application in Cu(In, Ga)Se2 (CIGS) thin film solar cells as back electrodes. Properties of these Mo thin films are systematically investigated. It is found that the dynamic deposition rate increases with the increasing discharge power while decreases with the increasing working pressure. The highest dynamic deposition rate of 15.1 nm m/min is achieved for the Mo thin film deposited at the discharge power of 1200 W and at the working pressure of 0.15 Pa. The achieved lowest resistivity of 3.7 × 10−5 Ω cm is attributed to the large grains in the compact thin film. The discharge power and working pressure have great influence on the sputtered Mo thin films. High efficiency of 12.5% was achieved for the Cu(In, Ga)Se2 (CIGS) thin film solar cells with Mo electrodes prepared at 1200 W and low working pressures. By further optimizing material and device properties, the conversion efficiency has reached to 15.2%.
Depla D., Leroy W.P.
Thin Solid Films scimago Q2 wos Q3
2012-08-01 citations by CoLab: 97 Abstract  
The Monte Carlo code SIMTRA, simulating the transport of atoms from the source to the substrate during physical vapor deposition (PVD), is used in several case studies to highlight important issues related to thin film sputter deposition. Atom collisions during gas-phase transport affect the energy distribution and the deposition profile of sputtered atoms. The model is compared with published models for the thermalization of sputtered atoms, and some features of this process are discussed. The vacuum chamber design can be easily implemented in the Monte Carlo code, and this possibility is used to discuss the use of shutters and masks, and the influence of the deposition geometry. The code can also be used to predict the composition when combing different sources, segmented targets, and during combinatorial synthesis of thin films. As the details of the transport are described, the velocity and the density of the gas-phase atoms can be calculated which can assist in the interpretation of several spectroscopic techniques such as laser induced fluorescence. Not only the energy loss of the transported atoms, but also their remaining energy upon arrival at the substrate is important as the incident energy strongly influences thin film growth. To illustrate the latter, the model is also used to study the growth of biaxially aligned thin films. The key parameters influencing the level of alignment can easily be retrieved using SIMTRA.
García-Martín J.M., Alvarez R., Romero-Gómez P., Cebollada A., Palmero A.
Applied Physics Letters scimago Q1 wos Q2
2010-10-25 citations by CoLab: 54 Abstract  
We show that the tilt angle of nanostructures obtained by glancing angle sputtering is finely tuned by selecting the adequate argon pressure. At low pressures, a ballistic deposition regime dominates, yielding high directional atoms that form tilted nanocolumns. High pressures lead to a diffusive regime which gives rise to vertical columnar growth. Monte Carlo simulations reproduce the experimental results indicating that the loss of directionality of the sputtered particles in the gas phase, together with the self-shadowing mechanism at the surface, are the main processes responsible for the development of the columns.
Anders A.
Thin Solid Films scimago Q2 wos Q3
2010-05-01 citations by CoLab: 689 Abstract  
An extended structure zone diagram is proposed that includes energetic deposition, characterized by a large flux of ions typical for deposition by filtered cathodic arcs and high power impulse magnetron sputtering. The axes are comprised of a generalized homologous temperature, the normalized kinetic energy flux, and the net film thickness, which can be negative due to ion etching. It is stressed that the number of primary physical parameters affecting growth by far exceeds the number of available axes in such a diagram and therefore it can only provide an approximate and simplified illustration of the growth condition?structure relationships.

Are you a researcher?

Create a profile to get free access to personal recommendations for colleagues and new articles.
Share
Cite this
GOST | RIS | BibTex
Found error?