Atomically precise graphene nanoribbons (GNRs) are a promising emerging class of designer quantum materials with electronic properties that are tunable by chemical design. However, many challenges remain in the device integration of these materials, especially regarding contacting strategies. We report on the device integration of uniaxially aligned and non-aligned 9-atom wide armchair graphene nanoribbons (9-AGNRs) in a field-effect transistor geometry using electron beam lithography-defined graphene electrodes. This approach yields controlled electrode geometries and enables higher fabrication throughput compared to previous approaches using an electrical breakdown technique. Thermal annealing is found to be a crucial step for successful device operation resulting in electronic transport characteristics showing a strong gate dependence. Raman spectroscopy confirms the integrity of the graphene electrodes after patterning and of the GNRs after device integration. Our results demonstrate the importance of the GNR-graphene electrode interface and pave the way for GNR device integration with structurally well-defined electrodes.

Unsupervised machine learning, and in particular data clustering, is a powerful approach for the analysis of datasets and identification of characteristic features occurring throughout a dataset. It is gaining popularity across scientific disciplines and is particularly useful for applications without a priori knowledge of the data structure. Here, we introduce an approach for unsupervised data classification of any dataset consisting of a series of univariate measurements. It is therefore ideally suited for a wide range of measurement types. We apply it to the field of nanoelectronics and spectroscopy to identify meaningful structures in data sets. We also provide guidelines for the estimation of the optimum number of clusters. In addition, we have performed an extensive benchmark of novel and existing machine learning approaches and observe significant performance differences. Careful selection of the feature space construction method and clustering algorithms for a specific measurement type can therefore greatly improve classification accuracies.

Graphene nanoribbons (GNRs) have attracted strong interest from researchers worldwide, as they constitute an emerging class of quantum-designed materials. The major challenges toward their exploitation in electronic applications include reliable contacting, complicated by their small size (<50 nm), and the preservation of their physical properties upon device integration. In this combined experimental and theoretical study, we report on the quantum dot behavior of atomically precise GNRs integrated in a device geometry. The devices consist of a film of aligned five-atom-wide GNRs (5-AGNRs) transferred onto graphene electrodes with a sub 5 nm nanogap. We demonstrate that these narrow-bandgap 5-AGNRs exhibit metal-like behavior at room temperature and single-electron transistor behavior for temperatures below 150 K. By performing spectroscopy of the molecular levels at 13 K, we obtain addition energies in the range of 200-300 meV. DFT calculations predict comparable addition energies and reveal the presence of two electronic states within the bandgap of infinite ribbons when the finite length of the 5-AGNR is accounted for. By demonstrating the preservation of the 5-AGNRs' molecular levels upon device integration, as demonstrated by transport spectroscopy, our study provides a critical step forward in the realization of more exotic GNR-based nanoelectronic devices.