Map magnetic response alongside phase and processing

Platform Characteristics

Deposition-to-characterization path.

01

Multi-element PVD

Co-sputter up to 7 elements onto a 100 mm wafer using DC, RF, pulsed DC, HiPIMS, or reactive sputtering.

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 magnetic multilayers, coercivity screens, reduced-rare-earth directions, and ferromagnetic shape-memory directions where thin-film screening fits the project.

Experimental plan

Prepare graded magnetic films, measure phase and composition, screen magnetic response, and select regions for tests in a specific layer stack.

Examples

Magnetic multilayers
Coercivity screens
Reduced-rare-earth directions
Ferromagnetic shape-memory directions

Methods used

graded target or co-sputter design
MOKE mapping
XRD mapping
composition mapping
post-treatment comparison

Measurements

magnetic response
coercivity
composition
phase
texture
annealing response

Outputs

magnetic response maps
composition ranges
annealing conditions
layer-stack test samples

What comes back: Measured magnetic film ranges for tests in the target layer stack.

Figures

Magnetic-screening method figures.

High-throughput characterization methods for thin-film material libraries. — Library-scale characterization
Composition, structure, magnetic, electrical, optical, mechanical, and microstructure measurements feed measured maps.Ludwig, npj Comput. Mater. 2019, Fig. 2

XRD dataset visualization and latent-space grouping. — XRD dataset visualization
Library-scale diffraction data are grouped to compare phase behavior across measured thin-film samples.Banko et al., npj Comput. Mater. 2021, Fig. 2

Closest Evidence

Relevant method demonstrations.

Banko et al., Commun. Mater. 2020

Process-microstructure maps

Processing-library work documents links between deposition parameters and microstructure.Open source

Banko et al., npj Comput. Mater. 2021

XRD analysis at library scale

Automated XRD analysis supports phase classification across large thin-film datasets.Open source

Platform Basis

Methods behind the screen.

Ludwig, npj Comput. Mater. 2019

Combinatorial thin-film synthesis, high-throughput characterization, data handling, and composition-property mapping.Open source

Banko and Ludwig, ACS Comb. Sci. 2020

Experimental materials data management for linked samples, metadata, and analysis workflows.Open source

References

Cited sources.

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.; 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.

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.

Banko, L.; Ludwig, A. Fast-Track to Research Data Management in Experimental Material Science-Setting the Ground for Research Group Level Materials Digitalization. ACS Comb. Sci. 2020, 22 (8), 401-409.

Experimental materials data management for linked samples, metadata, and analysis workflows.

All applications Evidence library

Map magnetic response alongside phase and processing.

Deposition-to-characterization path.

Multi-element PVD

Physical sample library

Composition map

Structure and properties

Scoped follow-up

Next experiment

Where this applies.

Relevant systems include magnetic multilayers, coercivity screens, reduced-rare-earth directions, and ferromagnetic shape-memory directions where thin-film screening fits the project.

Prepare graded magnetic films, measure phase and composition, screen magnetic response, and select regions for tests in a specific layer stack.

Magnetic-screening method figures.

Relevant method demonstrations.

Process-microstructure maps

XRD analysis at library scale

Methods behind the screen.

Cited sources.

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).

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).

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).

Banko, L.; Ludwig, A. Fast-Track to Research Data Management in Experimental Material Science-Setting the Ground for Research Group Level Materials Digitalization. ACS Comb. Sci. 2020, 22 (8), 401-409.