iPEN (Innovative Photonics Education in Nanotechnology) aims to provide an education training program to young researchers (postgraduate, Research students) in the most common used photonic tools and techniques in a Nanotechnology Laboratory. The project will include the development of online & offline modules, as well as, the organization of intensive courses, that will foster the photonic learning skills and build the confidence of young researchers in the field of Nanotechnologies.
The iPEN project targets to cultivate and offer training in three sections: (1) in photonics skills requested from the nanotechnology and market needs, (2) in soft skills most requested from the market needs; and (3) in teaching, offline and online, skills of the academics in order to become better teachers.
Principal Investigator
Research Associates
Alumni
While large-area crystal growth techniques, such as CVD, are successfully used for the production of GRMs, the presence of grain boundaries, vacancies and differently oriented grains, arising in such growths, substantially affect the crystal quality. This is unavoidably reflected in the physical properties of the GRMs which by definition depend stronger on the interatomic position of the few neighboring atoms as compared to bulk materials. There is currently no easily applicable, non-invasive, fast characterization method for determining with high-resolution these grain boundaries and orientations, over a large sample area. Our goal is to assess whether or not an optical technique could serve as a robust tool for early identification of common imperfections in the crystal structure of GRMs, during production. Furthermore, to get the produced GRMs back to the tray and provide quantitative feedback in real time, so that one can optimize crystal quality while still performing the growth. For this purpose, we will use polarization resolved second-harmonic generation (PSHG) optical microscopy for the eventual mapping of grain boundaries and crystal orientations, thus determining optically the crystalline quality of the produced GRMs.
The main goal of the project is to assess whether or not an optical technique could serve as a robust tool for early identification of common imperfections in the crystal structure of GRMs, during production. Furthermore, the goal is to get the produced GRMs back to the tray and provide quantitative feedback in real time, so that one can optimize crystal quality while still performing the growth.
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IQONIC will offer a scalable zero defect manufacturing platform covering the overall process chain of optoelectrical parts. IQONIC covers the design of new optoelectrical components and their optimized process chain, their assembly process, as well as their disassembly and reintroduction into the value chain. IQONIC will therefore comprise new hardware and software components interfaced with the current facilities through internet of things and data management platforms, while being orchestrated through scalable strategies at component, work-station and shopfloor level.
DIAGNOSE to early detect the different characteristics of the new part to be produced in terms of material sensitivity and product design parameters,
PREVENT to prevent the defect generation by recalibrating the production line, as well as defect propagation in later stages of the production,
PREDICT to predict the defect generation and the expected quality, allowing modifications to the parameters before the production of the products,
SUSTAIN to plan the reworking or remanufacturing of the product, if this is possible, and its re-use and/or requalification.
ADJUST to adapt the process chains to the specific production requirements of each new part through an iterative process until the quality is acceptable.
MANAGE to manage the aforementioned strategies through event modelling, KPI monitoring and real-time decision support system.
DETECT to early detect the defect, to adapt the part parameters to the previous successful state and plan to send it either to downstream or upstream stage.
REDESIGN to provide feedback for the design performance and knowledge to future parts and iterations to better products and process chain.