Quantitative infrared near-field imaging of suspended topological insulator nanostructures

The development of nanoscale solid-state devices exploiting the promising topological surface states of topological insulator materials requires careful device engineering and improved materials quality. For instance, the introduction of a substrate, device contact or the formation of oxide layers can cause unintentional doping of the material, spoiling the sought-after properties. In support of this, nanoscale imaging tools can provide useful materials information without the need for complex device fabrication. Here we study Bi2Se3 nanoribbons suspended across multiple material stacks of SiO2 and Au using infrared scattering scanning near-field optical microscopy. We validate our observations against a multilayer finite dipole model to obtain quantitative imaging of the local Bi2Se3 properties that vary depending on the local environment. Moreover, we identify experimental signatures that we associate with quantum well states at the Bi2Se3 surfaces. Our approach opens a new direction for future engineering of nanoelectronic devices based on topological insulator materials. Topological insulators (TIs) are an exciting class of materials that may find applications in a wide range of electronic and quantum devices, where their unique topologically protected surface states are exploited for new functionalities [1, 2]. However, synthesising materials and building practical devices remains a significant challenge. Much of this can be attributed to unintentional charge doping and defects in materials resulting in the bulk dominating the electron transport, and obscuring the topological surface contribution. In addition to intrinsic charge doping by impurities or vacancies, the position of the Fermi level will be influenced by the dielectric (substrate) material on which the TI material is located [3]. This issue is particularly relevant for thin TI materials, where the intrinsic bulk contribution is otherwise generally minimised by reduced dimensionality (’bulk-free’) [4–7], and band bending will dope device surfaces differently [3, 8]. Selecting the right materials in connection to the TI is therefore important to fully exploit topological surface states. A particularly promising route for addressing this problem is the growth of single crystalline TI nanoribbons (TINRs) [9, 10]. These can easily be mechanically transferred to any substrate for further study and device fabrication [11]. Yet, device fabrication remains challenging, and obtaining transport data on a statistically relevant set of nanoribbons is very time consuming. To this end, rapid room-temperature scanning probe characterisation tools could aid in the search and characterisation of materials. Near-field optical microscopy techniques have recently emerged as effective probes of the doping of TI materials due to intrinsic defects [12, 13] and even as potential probes of the topological surface states [14]. Near-field microscopy is also a powerful tool to map surface plasmons expected to arise from the metallic TI surface states, with many applications in sub-wavelength optical devices [15–18]. In all these cases, careful modelling of the dielectric environment of the TI material is key to quantitative imaging and extraction of material properties. Here we combine scattering scanning near-field optical microscopy (sSNOM) with theoretical modelling using a finitedipole model (FDM) extended to multiple dielectric layers to show that local electronic properties can be extracted in TINRs extending across multiple dielectric stacks, where different substrates can induce local variations in the TINR properties. We outline the methodology for quantitative evaluation of nanoribbon properties using sSNOM and demonstrate a route towards disentangling the impact of nanoribbon substrates on material properties by suspending the TINRs across multiple substrates. We use Bi2Se3 TI nanoribbons grown on a glass substrate using the catalyst-free Physical Vapour Deposition (PVD) ar X iv :2 11 2. 10 10 4v 1 [ co nd -m at . m es -h al l] 1 9 D ec 2 02 1