The challenges and major HiCONNECTS objectives are to transform the centralized cloud platform to decentralized platforms which include edge cloud computing in a sustainable, energy-efficient way. This will bring cloud services including Artificial Intelligence (AI) closer to the IOT end-users, which enables them to really use the COT and IOT efficiently. The technologies underpinning this revolutionary step include the development of high-performance computing, storage infrastructure, network interfaces and connecting media , and the analysis of IOT sensors and big data in real-time. This major step forward will enable, for example, the mobile clients (during the 5G deployment phase and 6G exploration) to move among different places with minimum cost, short response time and with stable connection between cloud nodes and mobile devices. The main underlying technology to be developed by the HiCONNECTS consortium, comprising large industrial players, universities and RTOs, and many SMEs, can be summarized under the title: heterogenous integration (HI) which is needed to meet the computing power, bandwidth, latency and sensing requirements for the next generation cloud and edge computing and applications. The HI revolution brings the electronic components and systems (ECS) into a new domain, which combines traditional silicon wafers integrated circuit (IC), InP based high speed electronics , and Si and InP photonics devices and interconnect. The HiCONNECTS ambition is to demonstrate, through HI development, a leap in computing and networking reliability and performances across the full vertical and horizontal ECS value chain (i.e. essential capabilities and key applications) in a sustainable way. In addition, HiCONNECTS will focus on the development of next generation design, algorithms, equipment (HW/SW), systems and Systems of Systems (SOS).

The metrology domain (which could be considered as the ‘eyes and ears’ for both R&D&I and production) is a key enabler for productivity enhancements in many industries across the electronic components and system (ECS) value chain and have to be an integral part of any Cyber Physical Systems (CPS) which consist of metrology equipment, virtual metrology or Industrial internet of things (IIoT) sensors, edge and high-performance computing (HPC). The requirements from the metrology is to support ALL process steps toward the final product. However, for any given ECS technology, there is a significant trade-off between the metrology sensitivity, precision and accuracy to its productivity. MADEin4 address this deficiency by focusing on two productivity boosters which are independent from the sensitivity, precision and accuracy requirements: • Productivity booster 1: High throughput, next generation metrology and inspection tools development for the nanoelectronics industry (all nodes down to 5nm). This booster will be developed by the metrology equipment’s manufacturers and demonstrated in an industry 4.0 pilot line at imec and address the ECS equipment, materials and manufacturing major challenges (MASP Chapter 15, major challenges 1 – 3). • Productivity booster 2: CPS development which combines Machine Learning (ML) of design (EDA) and metrology data for predictive diagnostics of the process and tools performances predictive diagnostics of the process and tools performances (predictive yield and tools performance). This booster will be developed and demonstrated in an industry 4.0 pilot line at imec, for the 5nm node, by the EDA, computing and metrology partners (MASP Chapter 15, major challenge 4). The same CPS concept will be demonstrated for the ‘digital industries’ two major challenges of the nanoelectronics (all nodes down to 5nm) and automotive end user’s partners (MASP Chapter 9, major challenges 1and 3).