Unsupervised machine learning (ML), and in particular data clustering, is a powerful ap-proach for the analysis of datasets and identification of characteristic features occurring throughout a dataset. It is gaining popularity across scientific disciplines and is particular-ly useful for applications without a priori knowledge of the data structure. In our lab, we have developed several ML approaches for the classification of univariate measurements and apply it to the field of nanoelectronics and spectroscopy. This allows us to identify meaningful structures in data sets, providing physically relevant information about the system under study. 

Opto-electronic characterization of strain effects in graphene nanoribbons

 

Graphene nanoribbons (GNRs) are nanometers-sized have attracted a strong interest from researchers worldwide as they constitute an emerging class of quantum materials. They exhibit novel physical properties beyond graphene such as a largely tuneable bandgap, optical, magnetic and topological effects, all tailorable by their edge structure. Recently, GNRs have been integrated into field-effect transistor devices, exhibiting high on/off rati-os and quantum dot behavior at cryogenic temperatures. Theoretically, applying strain on GNRs has been been predicted to results in largely tunable bandgaps, as well as the ap-pearance of localized edgestate.

 

The aim of this project is to investigate strain effects in GNRs both electrically and using Raman spectroscopy. The GNRs will be transferred onto a flexible substrate that will be bend using a mechanically controllable break junction setup. This home-designed setup is installed in a cryostat for the investigation of temperature-dependent charge transport measurements. In addition, Raman spectroscopy will be used to identify the various vibra-tional modes present in GNRs and track their energy upon straining. The GNRs will be provided by the group of Prof. Roman Fasel at Empa. The student will learn: