Graphene-based conductor materials (GCMs) consist of stacked and decoupled layers of graphene flakes and could potentially transfer graphene’s outstanding material properties like its exceptional electrical conductivity to the macro scale, where alternatives to the heavy and expensive metallic conductors are desperately needed. To reach super-metallic conductivity however, a systematic electrical conductivity optimization regarding the structural and physical input parameters is required. Here, a new trend in the field of process and material optimization are data-based models which utilize data science methods to quickly identify and abstract information and relationships from the available data. In this work such data-based models for the conductivity of a real GCM thin-film sample are build on data generated with an especially improved and extended version of the network simulation approach by Rizzi et al. [1, 2, 3]. Appropriate methods to create data-based models for GCMs are thereby introduced and typical challenges during the modelling process are addressed, so that data-based models for other properties of GCMs can be easily created as soon as sufficient data is accessible. Combined with experimental measurements by Slawig et al. [4] the created data-based models allow for a coherent and comprehensive description of the thin-films’
electrical parameters across several length scales.:List of Figures
List of Tables
Symbol Directory
List of Abbreviations
1 Introduction
2 Simulation approaches for graphene-based conductor materials
2.1 Traditional simulation approaches for GCMs
2.1.1 Analytical model for GCMs
2.1.2 Finite element method simulations for GCMs
2.2 A network simulation approach for GCMs
2.2.1 Geometry generation
2.2.2 Electrical network creation
2.2.3 Contact and probe setting
2.2.4 Conductivity computation
2.2.5 Results obtained with the network simulation approach
2.3 An improved implementation for the network simulation
2.3.1 Rizzi’s implementation of the network simulation approach
2.3.2 An network simulation tool for parameter studies
2.3.3 Extending the network simulation approach for anisotropy investigations and multilayer flakes
3 Data-based material modelling
3.1 Introduction to data-based modelling
3.2 Data-based modelling in material science
3.3 Interpretability of data-based models
3.4 The data-based modelling process
3.4.1 Preliminary considerations
3.4.2 Data acquisition
3.4.3 Preprocessing the data
3.4.4 Partitioning the dataset
3.4.5 Training the model
3.4.6 Model evaluation
3.4.7 Real-world applications
3.5 Regression estimators
3.5.1 Mathematical introduction to regression
3.5.2 Regularization and ridge regression
3.5.3 Support Vector Regression
3.5.4 Introducing non-linearity through kernels
4 Data-based models for a real GCM thin-film
4.1 Experimental measurements
4.2 Simulation procedure
4.3 Data generation
4.4 Creating data-based models
4.4.1 Quadlinear interpolation as benchmark model
4.4.2 KR, KRR and SVR
4.4.3 Enlarging the dataset
4.4.4 KR, KRR and SVR on the enlarged training dataset
4.5 Application to the GCM sample
5 Conclusion and Outlook
5.1 Conclusion
5.2 Outlook
Acknowledgements
Statement of Authorship
| Identifer | oai:union.ndltd.org:DRESDEN/oai:qucosa:de:qucosa:75630 |
| Date | 13 August 2021 |
| Creators | Rothe, Tom |
| Contributors | Stoll, Martin, Schuster, Jörg, Technische Universität Chemnitz, Fraunhofer Institut für elektronische Nanosysteme (ENAS) |
| Source Sets | Hochschulschriftenserver (HSSS) der SLUB Dresden |
| Language | English |
| Detected Language | English |
| Type | info:eu-repo/semantics/updatedVersion, doc-type:masterThesis, info:eu-repo/semantics/masterThesis, doc-type:Text |
| Rights | info:eu-repo/semantics/openAccess |
Page generated in 0.0026 seconds