About this Abstract |
| Meeting |
2027 TMS Annual Meeting & Exhibition
|
| Symposium
|
Phase Stability, Phase Transformations, and Reactive Phase Formation in Electronic Materials XXVI
|
| Presentation Title |
Machine Learning-Based Analysis of Residual Stress States in Nanocrystalline Thin Films |
| Author(s) |
Roland Brunner, Rahulkumar Jagdishbhai Sinojiya, Charlotte Cui |
| On-Site Speaker (Planned) |
Roland Brunner |
| Abstract Scope |
Metallic thin-films are essential for a wide range of applications, ranging from energy storage to high-power semiconductors. Here, the consideration of the correlation between microstructure, processing and the thin film property is of utmost importance. Thin films show highly attractive material properties, such as high hardness, strength and wear resistance. However, a big drawback concerns the emerging large residual stresses that result from their fabrication by deposition. The engineering of the microstructure allows to control the residual stress states, which in turn affect their performance and properties.
In this paper, we discuss the impact of minority element concentration on residual stresses that emerge after deposition in tungsten-titanium films with different titanium concentrations as well as study the influence of tramp elements on the microstructural equilibration in electroplated Cu-thin-films. We illustrate how our developed analysis workflow incorporating local residual stress measurements, structural as well as chemical characterization, physical modeling and machine learning for data extraction and stress prediction fosters the engineering of improved thin-films. |
| Proceedings Inclusion? |
Planned: |
| Keywords |
Characterization, Thin Films and Interfaces, Electronic Materials |