The semiconductor business has been identified as one of the key enabling technologies for european industrial development by European Comission. LFoundry contributes by developing research projects of european interest through its participation in schemes launched by the commission to address the areas of development identified.
Obiettivo: Migliorare la gestione, la formazione, la produttività e la soddisfazione lavorativa delle risorse umane attraverso l'uso strategico dell'intelligenza artificiale nel personalizzare i percorsi formativi dei dipendenti, tenuto conto delle personali competenze ed aspettative.
Obiettivo: Sviluppare un insieme di tecnologie e metodologie che consentano all’azienda di operare in accordo agli standard di qualità ed affidabilità richiesti dal mercato negli ambiti: A) Fault Detection and Classification (FDC); B) Machine Learning; C) Integrated System for Data Analysis; D) Manufacturing Execution System (MES) Integration.
Obiettivo: Sviluppare soluzioni tecnologiche innovative per ampliare il portfolio di prodotti optoelettronici e per applicazioni industriali. Migliorare la qualità dei prodotti e l’efficienza della linea di produzione in modo da poter approcciare in maniera più concreta nuovi clienti e settori del mercato dei semiconduttori.
Obiettivo: Sviluppare soluzioni tecnologiche negli ambiti: A) dispositivi di potenza Power-MOSFET (PMOS), per le declinazioni low e medium voltage split gate (LV/MV-SGT), e dispositivi bipolari a canale isolato (IGBT); B) sensoristica per immagini (CMOS Image sensors (CIS), Single Photon Avalanche Diodes (SPAD), Silicon Photo-multipliers(SIPM) e sensori ad effetto Hall; C) Silicon Photonics. Ed inoltre supportare l’evoluzione aziendale verso la completa digitalizzazione ed il miglioramento della qualità dei prodotti tramite sviluppo di attività di Machine learning nella linea di produzione.
Obiettivo: Sviluppare soluzioni di Machine Learning per l’efficientamento dei processi e delle operazioni nell’ambito dell’industria manifatturiera di semiconduttori, da realizzarsi tramite: A) Progettazione di soluzioni di Machine Learning e Deep Learning per aumentare la produttività e la qualità nella produzione dei semiconduttori (SC); B) Applicazione degli algoritmi: supporto all'integrazione per casi d'uso; C) Rendere l’intelligenza artificiale (IA) in ambito industriale interpretabile e affidabile.
The Project, coordinated by Thales Alenia Space Italia and in collaboration with the SME company ELITAL, concerns the construction and development of prototypes in proprietary technology LFoundry at 150nm of SoC (System on Chip) multi-purpose and Antenna for Avionic and Space application.
Area di specializzazione: Biologia molecolare. Soggetto Capofila: Università degli Studi del Sannio. CUP F26C18000170005 CMOS probes for mRNA fluorescence signal detection. The activity concerns the specialization of image sensors, starting with a prototype manufactured by LFoundry.
Area di specializzazione: Aerospazio. Soggetto Capofila: THALES ALENIA SPACE ITALIA S.P.A., CUP: D36C18000950005 avionics components for space, through the use of photonic technologies and the increase of electronic integration, from the present development of discrete component subsystems to systems developed on single chip (SoC System on Chip).
Sviluppo di tecnologie a semiconduttori su nodi tecnologici a 110/150 nm per realizzare dispositivi allo stato solido integrati (Development of semiconductor technologies on 110/150 nm technological nodes to create integrated solid state devices). The aim of the project is to equip LFoundry with a series of technological platforms that will allow it to diversify its product offer in such a way as to be able to intercept new market areas and new customers..
The main objective of Productive4.0 is to achieve improvement of digitizing the European industry by electronics and ICT. Ultimately, the project aims at suitability for everyday application across all industrial sectors – up to TRL8. It addresses various industrial domains with one single approach of digitalization. What makes the project unique is the holistic system approach of consistently focusing on the three main pillars: digital automation, supply chain networks and product lifecycle management, all of which interact and influence each other. This is part of the new concept of introducing seamless automation and network solutions as well as enhancing the transparency of data, their consistence and overall efficiency. Currently, such a complex project can only be realized in ECSEL.
The LIFE BITMAPS project will establish a pilot plant that will demonstrate a new and never-before attempted process for the treatment of effluents from electronics and semiconductor manufacturing. The project will contribute to the implementation of the EU Water Framework Directive 2000/60/EC by introducing more efficient treatment technologies that will help reduce Tetramethylammonium hydroxide (TMAH) pollution at source. By recycling wastewater, it will also demonstrate the application in practice of the circular economy priority of water reuse and savings in industrial processes See more at: www.lifebitmaps.eu
The main objective of the LASSIE-FP7 project is to implement large-area and low-cost intelligent SSL modules with high efficiency and high lighting quality, while assessing environmental impact throughout their life cycle.
The goal of this project is to develop the technology foundation for advanced optical microscope imaging at a resolution beyond the Rayleigh limit, utilising super-twinning photon states (N-partite entangled states) with de Broglie wavelength at a fraction of the wavelength of a photon in a classical state.
The purpose of this project is to promote best practices (for example, by following up standards) and to identifying gaps in methodologies and potential directions for further investigation to support risk assessment in order to protect human health. The project also aims to initiate communication with stakeholders to support informed decision making and the governance of risks related to the handling of nanomaterials and medical surveillance of the workforce in the semiconductor fabrication process.