Protective coatings

Screen conductive protective coatings and layer-stack candidates.

We test coating and interface libraries to map corrosion potential, contact resistance, mechanical response, and surface change before hardware or package-level tests.

Material question

Which coating, bonding layer, adhesion layer, barrier, current-collector film, or other layer-stack candidate keeps corrosion potential and contact resistance in range?

Output

coating composition ranges, corrosion-potential maps

Platform Characteristics

Deposition-to-characterization path.

Multi-element PVD

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

Physical sample library

Create a real composition-spread thin-film library with 342 registered measurement positions.

Composition map

Map element ratios by EDX/EDS or WDX for the material system.

Structure and properties

Measure XRD phase data and selected electrical, mechanical, optical, magnetic, or electrochemical response.

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.

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 conductive protective surfaces, bipolar-plate coating directions, adhesion layers, current collectors, bonding layers, and films or interfaces near layer-stack integration.

Experimental plan

Prepare coating or interface libraries, map composition and structure, measure corrosion potential with scanning droplet cell (SDC) and contact resistance with four-point probe, then select samples for reliability tests.

Examples

Bipolar plate coating directions
Conductive protective surfaces
Adhesion and bonding layers
Current-collector, bonding, and layer-stack films

Methods used

co-sputtered alloy libraries
reactive sputtering
scanning droplet cell (SDC)
four-point probe
XRD mapping
nanoindentation

Measurements

composition
phase
corrosion potential
contact resistance
hardness or modulus
surface change

Outputs

coating composition or process ranges for follow-up
corrosion-potential maps
contact-resistance maps
samples for coupon or package-level tests

What comes back: Measured coating and interface samples for hardware reliability, package-level reliability, or production qualification tests.

Figures

Coating and process figures.

Combinatorial thin-film workflow for materials-library studies. — Combinatorial thin-film workflow
A material library connects synthesis, high-throughput characterization, data handling, and candidate selection.Ludwig, npj Comput. Mater. 2019, Fig. 1

Structure-zone maps across aluminum content and deposition temperature. — Structure-zone diagram
Measured and predicted microstructure classes define process ranges for thin-film samples.Banko et al., Commun. Mater. 2020, Fig. 6

Closest Evidence

Closest coating and surface demonstrations.

Buenconsejo et al., ACS Comb. Sci. 2012

Twenty-four libraries for irreversible tests

Multiple ternary thin-film libraries were prepared on one substrate for tests such as etching, annealing, and exposure.Open source

Banko et al., ACS Comb. Sci. 2019

Cr-Al-N coating process maps

Reactive sputtering, XRD, plasma diagnostics, hardness, and elastic modulus were connected for coating development.Open source

RUB ELAN nanoelectrochemistry

Local electrochemical access

Scanning electrochemical methods support localized electrochemical measurements on defined regions.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 et al., Commun. Mater. 2020

Processing libraries and generative machine learning used to select thin-film microstructure ranges.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.

Buenconsejo, P. J. S.; Siegel, A.; Savan, A.; Thienhaus, S.; Ludwig, A. Preparation of 24 Ternary Thin Film Materials Libraries on a Single Substrate in One Experiment for Irreversible High-Throughput Studies. ACS Comb. Sci. 2012, 14 (1), 25-30.

Twenty-four ternary thin-film libraries on one substrate for irreversible tests.

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.

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.

RUB ELAN nanoelectrochemistry

Scanning electrochemical methods for local measurements on defined material regions.

All applications Evidence library