Combinatorial Deposition of Complex Oxide Thin Films: Unlocking New Materials and Functional Properties
When it comes to the quest for innovative materials with enhanced functional properties, the combinatorial investigation of oxide thin film deposition is a game-changer. Oxide thin films present unique opportunities, as a significant proportion of periodic elements can bind with oxygen to form a diverse array of oxides. This diverse oxide material world provides a playground for scientists who have been optimizing a growing list of functional materials, whether they are simple oxides (doped or undoped) or multi-element oxide phases, with homogeneous or segregated structures such as perovskites or grain composites.
These oxide materials exhibit a wide range of functional properties that find applications in various fields, including optics, microelectronics, catalysis, biology, and more. As the demand for miniaturization increases, thin film optimization becomes crucial. However, achieving this optimization is no small feat, as even slight variations in chemical composition, morphology, topography, or crystallinity can drastically alter their properties.
Combinatorial Investigation: An Efficient and Rapid Material Screening Tool
To address the vast universe of compositions that can be obtained by mixing different elements and the wide range of properties to evaluate, the traditional serial method of preparing and testing samples one at a time has become too time-consuming and expensive. This led to the emergence of combinatorial science in the 1990s for material investigation. Combinatorial science involves rapidly preparing and evaluating numerous samples, varying only in composition, either in the form of discrete composition libraries or continuous composition gradients (spreads).
One technological advancement in this field is the Chemical Beam Vapor Deposition (CBVD) Sybilla equipment developed by ABCD Technology, which offers precise control over combinatorial spreads. It achieves this by overlapping controlled flows of different element precursor molecules emitted by independent sources, defining the gradient pattern by the number and positions of these sources. This allows for the mixing of up to 5 elements with line-of-sight control, making it possible to calculate the flow composition accurately at any substrate position.
Material Optimization with CBVD
CBVD is currently at the forefront of optimizing various types of key material thin films. Examples include LiNbO3 for photonics, PZT for piezoelectric applications, doped NaTaO3 for green hydrogen production, composite films (Hf, Zr, Al, Ti, V) for microelectronics (high-k, supercapacitors, memristors), and (Si, Nb) doped TiO2 for transparent conductive electrodes.
For a complete, efficient process, combinatorial deposition should be paired with high-throughput characterization techniques and efficient data treatment, areas in which rapid improvements are driven by the emergence of Artificial Intelligence and Deep Learning techniques.