Combined atomic force microscopy and photoluminescence imaging to select single InAs/GaAs quantum dots for quantum photonic devices

Machine learning enhanced evaluation of semiconductor quantum dots

A key challenge in quantum photonics today is the efficient and on-demand generation of high-quality single photons and entangled photon pairs. In this regard, one of the most promising types of emitters are semiconductor quantum dots, fluorescent nanostructures also described as artificial atoms. The main technological challenge in upscaling to an industrial level is the typically random spatial and spectral distribution in their growth. Furthermore, depending on the intended application, different requirements are imposed on a quantum dot, which are reflected in its spectral properties. Given that an in-depth suitability analysis is lengthy and costly, it is common practice to pre-select promising candidate quantum dots using their emission spectrum. Currently, this is done by hand. Therefore, to automate and expedite this process, in this paper, we propose a data-driven machine-learning-based method of evaluating the applicability of a semiconductor quantum dot as single photon source. For this, first, a minimally redundant, but maximally relevant feature representation for quantum dot emission spectra is derived by combining conventional spectral analysis with an autoencoding convolutional neural network. The obtained feature vector is subsequently used as input to a neural network regression model, which is specifically designed to not only return a rating score, gauging the technical suitability of a quantum dot, but also a measure of confidence for its evaluation. For training and testing, a large dataset of self-assembled InAs/GaAs semiconductor quantum dot emission spectra is used, partially labelled by a team of experts in the field. Overall, highly convincing results are achieved, as quantum dots are reliably evaluated correctly. Note, that the presented methodology can account for different spectral requirements and is applicable regardless of the underlying photonic structure, fabrication method and material composition. We therefore consider it the first step towards a fully integrated evaluation framework for quantum dots, proving the use of machine learning beneficial in the advancement of future quantum technologies.

Deterministically fabricated solid-state quantum-light sources

The controlled generation of non-classical states of light is a challenging task at the heart of quantum optics. Aside from the mere spirit of science, the related research is strongly driven by applications in photonic quantum technologies, including the fields of quantum communication, quantum computation, and quantum metrology. In this context, the realization of integrated solid-state-based quantum-light sources is of particular interest, due to the prospects for scalability and device integration. This topical review focuses on solid-state quantum-light sources which are fabricated in a deterministic fashion. In this framework we cover quantum emitters represented by semiconductor quantum dots, colour centres in diamond, and defect-/strain-centres in two-dimensional materials. First, we introduce the topic of quantum-light sources and non-classical light generation for applications in photonic quantum technologies, motivating the need for the development of scalable device technologies to push the field towards real-world applications. In the second part, we summarize material systems hosting quantum emitters in the solid-state. The third part reviews deterministic fabrication techniques and comparatively discusses their advantages and disadvantages. The techniques are classified in bottom-up approaches, exploiting the site-controlled positioning of the quantum emitters themselves, and top-down approaches, allowing for the precise alignment of photonic microstructures to pre-selected quantum emitters. Special emphasis is put on the progress achieved in the development of in situ techniques, which significantly pushed the performance of quantum-light sources towards applications. Additionally, we discuss hybrid approaches, exploiting pick-and-place techniques or wafer-bonding. The fourth part presents state-of-the-art quantum-dot quantum-light sources based on the fabrication techniques presented in the previous sections, which feature engineered functionality and enhanced photon collection efficiency. The article closes by highlighting recent applications of deterministic solid-state-based quantum-light sources in the fields of quantum communication, quantum computing, and quantum metrology, and by discussing future perspectives in the field of solid-state quantum-light sources.

GaAs droplet quantum dots with nanometer-thin capping layer for plasmonic applications

We report on the growth and optical characterization of droplet GaAs quantum dots (QDs) with extremely-thin (11 nm) capping layers. To achieve such result, an internal thermal heating step is introduced during the growth and its role in the morphological properties of the QDs obtained is investigated via scanning electron and atomic force microscopy. Photoluminescence measurements at cryogenic temperatures show optically stable, sharp and bright emission from single QDs, at visible wavelengths. Given the quality of their optical properties and the proximity to the surface, such emitters are good candidates for the investigation of near field effects, like the coupling to plasmonic modes, in order to strongly control the directionality of the emission and/or the spontaneous emission rate, crucial parameters for quantum photonic applications.