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Title: Sputter-Deposited Mo Thin Films: Multimodal Characterization of Structure, Surface Morphology, Density, Residual Stress, Electrical Resistivity, and Mechanical Response

Journal Article · · Integrating Materials and Manufacturing Innovation

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. Here, 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.

Research Organization:
Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Sponsoring Organization:
USDOE National Nuclear Security Administration (NNSA); USDOE Laboratory Directed Research and Development (LDRD) Program; USDOE Office of Science (SC), Basic Energy Sciences (BES). Scientific User Facilities (SUF)
Grant/Contract Number:
NA0003525
OSTI ID:
2311620
Report Number(s):
SAND-2023-05488J
Journal Information:
Integrating Materials and Manufacturing Innovation, Vol. 12, Issue 2; ISSN 2193-9764
Publisher:
SpringerCopyright Statement
Country of Publication:
United States
Language:
English

References (17)

Perspective: Materials informatics and big data: Realization of the “fourth paradigm” of science in materials science journal April 2016
Experimental search for high-temperature ferroelectric perovskites guided by two-step machine learning journal April 2018
Evaluation of residual stress in sputtered tantalum thin-film journal May 2016
K α and K β x-ray emission spectra of copper journal January 1995
Stress, strain, and microstructure of sputter‐deposited Mo thin films journal October 1991
High-Throughput Machine-Learning-Driven Synthesis of Full-Heusler Compounds journal October 2016
Sputtered molybdenum thin films and the application in CIGS solar cells journal January 2016
High-resolution characterization of the forbidden Si 200 and Si 222 reflections journal March 2015
Investigation of thermal stability of Mo thin-films as the buffer layer and various Cu metallization as interconnection materials for thin film transistor–liquid crystal display applications journal June 2007
The Powder Diffraction File: a quality materials characterization database journal November 2019
Machine learning in materials informatics: recent applications and prospects journal December 2017
Bayesian-Driven First-Principles Calculations for Accelerating Exploration of Fast Ion Conductors for Rechargeable Battery Application journal April 2018
What is the Young's Modulus of Silicon? journal April 2010
Sputtered molybdenum films: Structure and property evolution with film thickness journal January 2014
New opportunities for materials informatics: Resources and data mining techniques for uncovering hidden relationships journal April 2016
SIMNRA, a simulation program for the analysis of NRA, RBS and ERDA
  • Mayer, M.
  • The fifteenth international conference on the application of accelerators in research and industry, AIP Conference Proceedings https://doi.org/10.1063/1.59188
conference January 1999
An improved technique for determining hardness and elastic modulus using load and displacement sensing indentation experiments journal June 1992