01
Oxides and nitrides
Connect oxide and nitride composition to phase and property response.
We test oxide and nitride libraries to measure stoichiometry, phase, hardness, conductivity, optical response, or catalytic response before selected-film tests.
Platform Characteristics
Deposition-to-characterization path.
02
Physical sample library
Create a real composition-spread thin-film library with 342 registered measurement positions.03
Composition map
Map element ratios by EDX/EDS or WDX for the material system.04
Structure and properties
Measure XRD phase data and selected electrical, mechanical, optical, magnetic, or electrochemical response.05
Scoped follow-up
Scanning droplet cell (SDC), SECCM, XPS, microscopy, or interface analysis can be added when surface change or a localized measurement decides the next step.06
Next experiment
Measured maps, Bayesian optimization, or Gaussian-process selection support repeat samples or a narrower campaign.Material decision
Where this applies.
Relevant areas
Relevant systems include transition-metal nitrides, functional oxides, perovskite oxides, hard coatings, transparent films, and catalyst-relevant oxide systems.
Experimental plan
Use reactive sputtering to create composition gradients, map composition and phase, measure the target property, and select narrower process or composition ranges.
Examples
- Cr-Al-N and related hard nitrides
- Functional oxides
- Perovskite oxide libraries
- Catalyst-relevant oxide films
Methods used
- reactive sputtering
- automated XRD
- EDX/EDS or WDX mapping
- nanoindentation
- UV-VIS
- four-point probe
Measurements
- stoichiometry
- phase
- texture
- hardness
- elastic modulus
- conductivity
- optical response
Outputs
- phase-property maps
- hardness and modulus maps
- oxide or nitride candidates for follow-up
- narrower follow-up campaigns
What comes back: Measured oxide or nitride ranges for coating, catalyst, hardware, or device tests.
Figures
Phase, process, and property figures.
Structure-zone diagram
Measured and predicted microstructure classes define process ranges for thin-film samples.Banko et al., Commun. Mater. 2020, Fig. 6
Library-scale characterization
Composition, structure, magnetic, electrical, optical, mechanical, and microstructure measurements feed measured maps.Ludwig, npj Comput. Mater. 2019, Fig. 2Closest Evidence
Closest published oxide and nitride demonstrations.
Banko et al., ACS Comb. Sci. 2019
Cr-Al-N composition and process study
Reactive sputtering, automated XRD, plasma diagnostics, hardness, and elastic modulus were measured for Cr-Al-N films.Open sourcePiotrowiak et al., Adv. Eng. Mater. 2023
Perovskite oxide property ranges
Oxide libraries connected phase formation with band gap, conductivity, and catalyst-relevant properties.Open sourceBanko et al., Commun. Mater. 2020
Processing-library microstructure model
Processing libraries linked deposition conditions to thin-film microstructure.Open sourcePlatform Basis
Methods behind the screen.
Ludwig, npj Comput. Mater. 2019
Combinatorial thin-film synthesis, high-throughput characterization, data handling, and composition-property mapping.Open sourceBanko et al., npj Comput. Mater. 2021
Deep-learning visualization and novelty detection for large XRD datasets from thin-film measurements.Open sourceReferences
Cited sources.
Banko, L.; Ries, S.; Grochla, D.; Arghavani, M.; Salomon, S.; Pfetzing-Micklich, J.; Kostka, A.; Rogalla, D.; Schulze, J.; Awakowicz, P.; Ludwig, A. Effects of the Ion to Growth Flux Ratio on the Constitution and Mechanical Properties of Cr1-x-Alx-N Thin Films. ACS Comb. Sci. 2019, 21 (12), 782-793.
Reactive DC magnetron sputtering, automated XRD, plasma diagnostics, hardness, and elastic modulus for Cr-Al-N films.
Piotrowiak, T. H.; Zehl, R.; Suhr, E.; Kohnen, B.; Banko, L.; Ludwig, A. Combinatorial Sputter Synthesis of Single-Phase La(XYZ)O3+-sigma Perovskite Thin-Film Libraries: A New Platform for Materials Discovery. Adv. Eng. Mater. 2023, 25 (16), 2300437.
Perovskite oxide thin-film libraries with phase, band-gap, conductivity, and catalyst-relevant screening.
Piotrowiak, T. H.; Wang, X.; Banko, L.; Kumari, S.; Sarker, S.; Mehta, A.; Ludwig, A. High-Throughput Characterization of (FexCo1-x)3O4 Thin-Film Composition Spreads. ACS Comb. Sci. 2020, 22 (12), 804-812.
Fe-Co-O composition spreads with phase, surface, optical, and electrode-stack-related characterization.
Banko, L.; Lysogorskiy, Y.; Grochla, D.; Naujoks, D.; Drautz, R.; Ludwig, A. Predicting structure zone diagrams for thin film synthesis by generative machine learning. Commun. Mater. 1, 15 (2020).
Processing libraries and generative machine learning used to select thin-film microstructure ranges.
Ludwig, A. Discovery of new materials using combinatorial synthesis and high-throughput characterization of thin-film materials libraries combined with computational methods. npj Comput. Mater. 5, 70 (2019).
Combinatorial thin-film synthesis, high-throughput characterization, data handling, and composition-property mapping.
Banko, L.; Maffettone, P. M.; Naujoks, D.; Olds, D.; Ludwig, A. Deep learning for visualization and novelty detection in large X-ray diffraction datasets. npj Comput. Mater. 7, 104 (2021).
Deep-learning visualization and novelty detection for large XRD datasets from thin-film measurements.