Project Title: ”Metrology Advances for Digitized ECS industry 4.0’”
Project Type: H2020 European Project
The objective of MADEin4 is to demonstrate Industry 4.0 manufacturing productivity improvement by developing advanced, highly productive and connected metrology cyber physical systems which combine metrology data analysis and design with machine learning methodologies and digital twinning.
The MADEin4 scope includes the establishment of an advanced ECS ‘Industry 4.0 predictive yield and tools performance’ pilot line that is centric to IMEC. Around that centre are grouped the metrology, design, diagnostics and computing equipment and modules manufacturers whose strategies and methodological approaches are interpreted and localized by semiconductors and automotive end users in Europe. In addition, MADEin4 includes ‘Industry 4.0’ ‘digitization of manufacturing’ and demonstrations by those European end users.
MADEin4 to bring production lines to a next level in productivity and predictability by focusing on two boosters; while fulfilling and/or exceeding also sensitivity, precision and accuracy requirements:
• Productivity booster 1: High throughput, next generation metrology and inspection tools development. This booster, yielding connected Cyber Physical Systems (CPS) enabling very high sampling and data rates, will be developed by the metrology equipment manufacturers, module suppliers and knowledge institutes and will be demonstrated in an industry 4.0 pilot line at IMEC. It will address major challenges for ECS equipment, materials and manufacturing industries.
• Productivity booster 2: Develop combinations of Design (EDA), Product/process Life Cycle Management (PLM), modelling, simulations and advanced metrology data analysis with Machine Learning (ML), Digital Twinning and predictive diagnostics of the process (predictive yield) and tools performance. This booster will be developed and demonstrated in an industry 4.0 pilot line at IMEC, by the EDA, and by computing and metrology partners. The digital twinning and predictive maintenance concepts will be demonstrated in two major ‘digital industries’: semiconductor industry and automotive production.