Heterogeneous electronic photonics integration for the realisation of quantum optical components – E-PhoQuant

Operation co-funded by EU cohesion policy (European Social Fund (ESF)) under the programme Thüringen - ESF (2014DE05SFOP014), managing authority: Thüringer Ministerium für Arbeit, Soziales, Gesundheit, Frauen und Familie, Referat 34 "Verwaltungsbehörde ESF". Beneficiary: Technische Universität Ilmenau. Location: Ilm-Kreis (NUTS DEG0T); Germany. Budget: total eligible expenditure EUR 422,653.93, as stated by the source. Runs 2021-02-01 to 2022-12-31. Source: Kohesio, EU cohesion policy projects managed by national and regional authorities (not CORDIS). Summary from Kohesio (English machine translation by the source): Within the framework of the research, a heterogeneous integration solution for the realisation of compact quantum optical components and modules will be developed. On the basis of a glass-ceramic composite technology developed at the Ilmenau InQuoSens site, a system-in-package platform is to be created with which highly integrated combined-microelectronic/optical systems can be generated. The system approach not only aims at miniaturisation, but also builds on wafer-level structuring technologies that have a high transfer potential.

Original source.

Source: Kohesio (EU cohesion policy projects), European Commission, Directorate-General for Regional and Urban Policy (DG REGIO). Retrieved 2026-10-21. Licence: CC0 1.0 (Cohesion Open Data Platform, dataset 2014-2020 Kohesio projects). Selected metadata, adapted presentation; the publisher's record is authoritative. No endorsement by the source is implied. Licence: public domain dedication (CC0 1.0).

AI-assisted summary: The project "E-PhoQuant" focuses on developing a heterogeneous integration solution for compact quantum optical components, funded by the EU's ESF. It is managed by Technische Universität Ilmenau in Germany, with a budget of EUR 422,653.93, running from February 2021 to December 2022. Suggested technology pillar: enabling. Generated by AI from the public source text; it may contain errors and the original source is authoritative.