Nanooptical Simulation and Machine Learning for Chip Metrology (NanoMaC) Reconstruction of nanostructures on the basis of simulations and machine learning. Chip production consists of a large number of process steps that must be carried out with high precision in order to produce all the desired structures on the wafers. Ellipsometry…

Multicore fiber single photon emitter module for quantum communication In the project "Multicore fiber single photon source" (MultiCoreSPS), innovative quantum chips for quantum communication with high transmission rates are being developed. The modules consist of semiconductor-based single photon sources that are directly coupled to multicore optical fibres. For the first…

Artificial Intelligence in Atom Interferometry (AI2) The goal of the project is to significantly improve the efficiency and robustness of atomic interferometry experiments by using artificial intelligence (AI). The AI methods are to be applied along the chain from the improvement of theory models for the description of the experiments…

Cost-efficient gas sensor system based on wavelength-tunable quantum dot VCSEL arrays. The goal of the Berlin-Polish project is to develop an innovative, low-cost, high-precision and high-reliability spectroscopic system capable of detecting in-situ low water vapor concentrations for industrial applications. In QD-Sense, innovative, low-cost, tunable and single-mode lasers and laser arrays…

AI powered Digital twin for lighting infrastructure in the context of front-end Industry 4.0 (AI-TWILIGHT) In all lighting sectors, warranty and customisation are becoming key product differentiators. In addition to that, the integration of more electronics and sensors in lighting systems will change what we call lighting today. While the…

Simulation and machine learning for high-precision dimensional microscopy (SiM4diM) In many industrial sectors, optical microscopes are used to measure micro- and nanostructures, as they enable non-destructive and rapid inspection of manufacturing processes. Due to wave-optical effects, such as light diffraction at object edges or unavoidable imaging errors of the optical…

Machine Learning for the Multi-Parameter Optimization of Atomic Quantum Sensors (OPTIMAL-QT) The optimization of experimental parameters in experiments with ultracold atoms is a long and complex process. The sheer number of possible parameters and correlations among them is difficult to grasp and often requires lengthy manual optimization by the senior…

Scalable Nano-Photonic Concepts and Stability of Hybrid Perovskite Semiconductors for Tandem Photovoltaics (SNAPSHOTS) The project focuses on three key objectives essential for the development of efficient and stable large-area hybrid tandem device technology: improving light harvesting in hybrid tandem solar cells, achieving long-term stability evaluation and accelerated device testing, as…

Quantifying Parameter Uncertainties and Sensitivities (QUPUS) The goal of the project is the development of a software toolbox for convenient numerical investigations regarding uncertainty quantication. Through the use of innovative numerical methods such as of the polynomial chaos method, the dependency between the simulation results and the uncertainties of the…

Fibre-coupled semiconductor single-photon source for secure quantum-communication in the 1.3 µm range (FI-SEQUR) In this project, the consortium partners jointly develop an efficient an efficient single-photon source with an optically excited semiconductor quantum dot as emitter providing single photons on demand. The device will be suitable for local-area secure data…