Abstract Scope |
Designing next generation thin films, tailor-made for specific applications, relies on the availability of robust processing-structure-property relationships. Traditional structure zone diagrams (SZDs) are limited to low-dimensional mappings, with machine-learning methods only recently relating multiple processing parameters to the final microstructure. Despite this progress, structure-processing relationships are unknown for processing conditions that vary during thin film deposition, limiting the range of microstructures/properties achievable. In this talk, we discuss how to use genetic algorithms (GAs) to design time-dependent processing protocols that achieve tailor-made microstructures. We simulate physical vapor deposition of a binary-alloy thin film via a phase-field model, where deposition rates and diffusivities of the deposited species are controlled via the GA. Our GA-guided protocols achieve targeted microstructures with lateral or vertical concentration modulations, as well as more complex, hierarchical microstructures previously not described in simple SZDs. Our algorithm provides insight to experimentalists looking for additional avenues to design novel thin-film microstructures. |