RF acoustic films

Constrain piezoelectric film ranges before resonator builds.

We test RF acoustic film libraries to measure phase, texture, stress indicators, leakage, and fit with the electrode or resonator layer stack before resonator-level tests.

Material question

Which AlN, AlScN, ScAlN, or related film region has the structure and process compatibility worth fabricating into a device?

Output

process ranges, samples for device tests

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 questions include composition and process ranges where phase purity, texture, electrode compatibility, and film stress decide the next experiment.

Experimental plan

Prepare composition or process gradients, measure structure and electrical indicators, then select regions for tests in a device layer stack or resonator.

Examples

AlN and doped AlN films
AlScN or ScAlN composition ranges
Texture and phase screens
Electrode-compatible layer stacks

Methods used

composition gradients
process gradients
XRD phase and texture mapping
four-point probe
follow-up depositions that keep the intended layer stack in mind

Measurements

phase
texture
thickness
stress indicator
leakage or resistance
electrode compatibility

Outputs

process ranges
samples for device tests
excluded phase regions
variables to test in the layer stack

What comes back: Measured composition and process ranges for resonator-level or device layer stack tests.

Figures

Process and structure figures.

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

Variational autoencoder workflow and latent-space plots for XRD patterns. — XRD latent-space analysis
Large diffraction datasets are organized by phase similarity and structure signals before regions are selected.Banko et al., npj Comput. Mater. 2021, Fig. 1

Closest Evidence

Relevant process and structure demonstrations.

Banko et al., Commun. Mater. 2020

Process-microstructure selection

Processing libraries linked deposition parameters to measured microstructure in thin films.Open source

Banko et al., npj Comput. Mater. 2021

XRD analysis at library scale

Large XRD datasets were organized to compare structure signals across measured samples.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.

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