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Process maps for both End Applications (D1.1)

Deliverable 1.1 - Process Maps for both End Applications was submitted 29/05/2017. This report explains how the Consortium succeeded in clearly drafting a realistic process map for both end applications  targeted by the project.


These process maps are the outcome of months of Consortium effort and contains information, which was gathered at Consortium physical and virtual meetings during the first six months of the Project. For each application, two process maps have been drafted: one corresponds to the most realistic approach and the other one to the most valuable approach for the end-users and the consortium.


The HiperLAM processes are being benchmarked in terms of throughput, speed and cost against the existing processes, which are currently employed by the project’s end-users for the manufacturing of the end-applications of interest. 


The final process maps are designed so that HiperLAM’s throughput, speed and cost reduction goals are facilitated. The RFID antenna fabrication complies with high-throughput, low cost scenario’s promise: 10x higher throughput and up to 20x cost reduction accomplished owing to the incorporation of HiperLAM’s technology in PRAG’s manufacturing process; the fingerprint sensor HiperLAM process will enable a 5x shorter lead time and more than 50% cost saving for FLEX, fulfilling the short lead time, low cost scenario promised by HiperLAM.


The data contained in the final process maps will facilitate the implementation of the fabrication process of both end applications in a manner consistent to the content of Annex 1 and aligned to HiperLAM’s speed increase and cost reduction goals. In collaboration with D1.2 “Specification Document for systems, materials and end applications”, this deliverable will constitute the roadmap for HiperLAM’s technology regarding the end applications, throughout the whole timeframe of the project.





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The HIPERLAM Project is an initiative of the Photonics and Factories of the Future Public Private Partnerships and received funding from the European Union's Horizon 2020 Research and Innovation Programme under Grant Agreement No. 723879
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