Technical Symposium PP
Symposium PP covers cutting-edge developments in plasma and vapor deposition technologies for advanced thin film synthesis and surface engineering. Topics include innovations in PVD, CVD, and plasma-assisted methods, along with in-situ diagnostics, plasma-surface interactions, and simulation-driven process control. The symposium also highlights emerging approaches such as data-enabled process optimization and greybox modeling, which combine physical and machine learning models to improve predictions of coating performance and wear behavior. Applications span protective coatings, electronics, energy, and biomedical devices, bridging fundamental research with industrial impact.
PP1. PVD Coating Technologies
This session solicits contributions related to the development of new PVD methods and the advancement of industrially applied technologies. Sputtering, cathodic arc, anodic arc, laser, and electron beam-based methods and their combinations are considered in particular. The session welcomes contributions incorporating topics such as in-situ measurements, plasma transport in electromagnetic fields, plasma diagnostics, and computer-aided process development. Furthermore, the session will cover digital methods to understand and control thin film deposition processes, encompassing topics such as simulations, small and large-scale data analysis, in-situ process feedback control, and real-time optimization related to PVD technologies. Potential application areas include deposition technologies in use for wear-protective coatings for components and tools, low-friction thin films, carbon-based coatings, high-temperature wear, erosion-, and corrosion-resistant coatings, optical layers, biomaterials, decorative coatings, and materials for energy applications.
PP1. Invited Speakers:
- TBA
PP2. HiPIMS, Pulsed Plasmas, and Energetic Deposition
The energy carried to the thin film during deposition is crucial in reducing the growth temperature and improving the properties of thin film materials. Higher plasma density leads to enhanced ionization of the film precursors and offers better deposition process control. This results in improved coating characteristics, valuable, e.g., optical, wear-resistant, or photovoltaic applications. This session solicits contributions from academia as well as industry and covers both the physics and the applications of energetic deposition. Topics of interest include plasma generation and discharge physics, plasma surface interaction and diagnostics, modeling and data-driven process understanding and simulations, reactive processes and process control, mechanisms of film growth, surface and interface engineering, industrial applications and production, upscaling, and associated equipment.
PP2. Invited Speakers:
- TBA
PP3. CVD, ALD, and Laser-based Deposition & Microfabrication Technologies
This session explores thin film deposition and microscale fabrication techniques based on chemical vapor deposition (CVD), atomic layer deposition (ALD), and laser/plasma-assisted methods. The processes covered include LPCVD, MOCVD, PECVD, PEALD, and laser/plasma-based structuring, enabling functional coatings, selective area processing, and nanostructured interfaces. Topics include novel precursors, in situ diagnostics, surface engineering, and multiscale modeling. Contributions addressing the theoretical modeling of the role of lasers and plasmas in chemical reactions that govern the film growth are also encouraged. The session highlights interdisciplinary strategies for structuring and engineering surfaces at micro- and nanoscales, enabling applications in electronics, energy, catalysis, and biomaterials.
PP3 Invited Speakers:
- TBA
PP4. Greybox Models for Process and Film Performance Prediction
The prognosis of the wear behavior of coated tools and components is still unsolved, and sufficiently accurate models to predict the wear behavior do not yet exist. Methods of machine learning in combination with conventional simulation approaches offer high potential to tackle this issue. By using these methods, the understanding of the wear mechanisms and the forecast of wear development and lifetime can benefit. Consequently, tool and component development as well as machining processes and applications can be improved by adjusting the process parameters or by adjusting the coatings deposited on the tools or components in order to achieve higher productivity and longer lifetime. Regarding wear prognosis, “Whitebox” models, based on physical laws and analytical correlations, represent the state of the art to determine the behavior of, for example, tools during the cutting process. Nevertheless, for a very complex and non-linear system behavior like the wear progress of tools, whitebox models are limited. One possibility to predict non-linear behavior is offered by data-driven “Blackbox” models, mostly based on machine learning algorithms. To utilize the benefits of whitebox and blackbox models and to overcome the limitations of both, they can be combined into “Greybox” models. This offers great potential to improve the prediction accuracy of wear and remaining service life. The session welcomes contributions that address the wear prognosis of coated tools or components by conventional simulation approaches in combination with data-driven models. Emphasis can be directly on greybox approaches or on particular analytical or data-driven models. Of particular relevance will be whitebox models that combine coating properties and behavior in processes and contribute to an increased prediction accuracy of established models. Also welcome are contributions to blackbox models for high-performance applications with coated tools or components. In particular, the need for blackbox models should be described, and how these models can be combined with whitebox models to form greybox models. Moreover, the greybox modelling approach can be extended to other applications of the coating industry in the future. Contributions to this topic from scientists and from industry in the fields of coating technology, production, mathematics, and information technology are very welcome to contribute to the session and present their work.
PP4 Invited Speakers:
- Jan Trieschmann, UNI Kiel, Germany, “Machine Learning Enhanced Plasma Simulations: From Plasma Processing to Thin Film Properties”