RESUMO
We report on multi-resonance chirped distributed Bragg reflector (DBR) microcavities. These systems are employed to investigate the light-mater interaction with both intra- and inter-layer excitons of transition metal dichalcogenide (TMDC) bilayer heterostructures. The chirped DBRs consisting of SiO2 and Si3N4 layers of gradually varying thickness exhibit a broad stopband with a width exceeding 600 nm. Importantly, the structures provide multiple resonances across a broad spectral range, which can be matched to resonances of the embedded TMDC heterostructures. Studying cavity-coupled emission of both intra- and inter-layer excitons from an integrated WSe2/MoSe2 heterostructure in a chirped microcavity system, an enhanced interlayer exciton emission with a Purcell factor of 6.67 ± 1.02 at 4 K is observed. The cavity-enhanced emission of the interlayer exciton is used to investigate its temperature-dependent luminescence lifetime of 60 ps at room temperature. The cavity system modestly suppresses intralayer exciton emission by intentional detuning, thereby promoting a higher IX population and enhancing cavity-coupled interlayer exciton emission. This approach provides an intriguing platform for future studies of energetically distant and confined excitons in different semiconducting materials, which paves the way for various applications such as microlasers and single-photon sources by enabling precise emission control and utilizing multimode resonance light-matter interaction.
RESUMO
We report on the deterministic fabrication of quantum devices aided by machine-learning-based image processing. The goal of the work is to demonstrate that pattern recognition based on specifically trained machine learning (ML) algorithms and applying it to luminescence maps can strongly enhance the capabilities of modern fabrication technologies that rely on a precise determination of the positions of quantum emitters like, for instance, in situ lithography techniques. In the present case, we apply in situ electron beam lithography (EBL) to deterministically integrate single InGaAs quantum dots (QDs) into circular Bragg grating resonators with increased photon extraction efficiency (PEE). In this nanotechnology platform, suitable QDs are selected by 2D cathodoluminescence maps before EBL of the nanoresonators aligned to the selected emitters is performed. Varying the electron beam dose of cathodoluminescence (CL) mapping, we intentionally change the signal-to-noise ratio of the CL maps to mimic different brightness of the emitters and to train the ML algorithm. ML-based image processing is then used to denoise the images for reliable and accurate QD position retrieval. This way, we achieve a significant enhancement in the PEE and position accuracy, leading to more than one order increase of sensitivity in ML-enhanced in situ EBL. Overall, this demonstrates the high potential of ML-based image processing in deterministic nanofabrication which can be very attractive for the fabrication of bright quantum light sources based on emitters with low luminescence yield in the future.