The goal of the PRIME project is to establish an open Ultra Low Power (ULP) Technology Platform containing all necessary design and architecture blocks and components which could enable the European industry to increase and strengthen their competitive and leading eco-system and benefit from market opportunities created by the Internet of Things (IoT) revolution. Over 3 years the project will develop and demonstrate the key building blocks of IoT ULP systems driven by the applications in the medical, agricultural, domestics and security domains. This will include development of high performance, energy efficient and cost effective technology platform, flexible design ecosystem (including IP and design flow), changes in architectural and power management to reduced energy consumption, security blocks based on PUF and finally the System of Chip and System in Package memory banks and processing implementations for IoT sensor node systems. Developped advanced as 22nm FDSOI low power technologies with logic, analog, RF and embedded new memory components (STT RAM and RRAM) together with innovative design and system architecture solutions will be used to build macros and demonstrate functionality and power reduction advantage of the new IoT device components. The PRIME project will realize several demonstrators of IoT system building blocks to show the proposed low power wireless solutions, functionality and performance of delivered design and technology blocks. The consortium semiconductor ecosystem (IDMs, design houses, R&D, tools & wafer suppliers, foundries, system/product providers) covers complementarily all desired areas of expertise to achieve the project goals. The project will enable an increase in Europe’s innovation capability in the area of ULP Technology, design and applications, creation of a competitive European eco-system and help to identify market leadership opportunities in security, mobility, healthcare and smart cost competitive manufacturing.

BIOSENSEI develops a real-time, multiplexed, end-to-end, tailored and reliable biosensor platform, using cellular responses, for detection of abiotic pollutants - Nutrients, Estrogenic endocrine-disrupting chemicals, and PFAS (Perfluoroalkyl and Polyfluoroalkyl Substances); and biotic pollutants - Microcystins. Cellular biosensors from bacterial variants will be genetically engineered using, RNA-RNA interactive and type III CRISPR-Cas-mediated transduction cascades. These biosensors are encapsulated and immobilised at bi-modal transducers (nanoelectrochemical and optical) to provide highly reliable, tuneable and sensitive detection of the target pollutants. Bespoke ultra-low power analog front ends and autonomous IoT end-nodes will enable operation and data acquisition from biosensors and facilitate easy integration in existing LoRa networks enabling real-time data feeds. Neural computing algorithms are embedded on the edge to correct for sensor aging and interferents in the (bio)chemical transduction and improve sensor data accuracy. An online dashboard will be developed to allow end users to visualize data. BIOSENSEI will embed the whole R&D process within a safe-and-sustainable-by-design framework to guarantee environmental safety related to risks of potential release into the open environment. Biosensors will be scalable, adaptable to different applications in water & soil and will be deployed in four different use-cases. The consortium is vertically integrated bringing expertise in cellular biology, surface chemistry, nanoelectronics fabrication, hardware integration, regulatory and industrial sampling and artificial intelligence. BIOSENSEI directly addresses HORIZON-CL6-2023-ZEROPOLLUTION-01-6 Biosensors and user-friendly diagnostic tools for environmental services and will allow cellular biosensors to be deployed outside laboratory settings for the first time the project and has the potential to considerably contribute to fulfil EU vision on zero-pollution.

With the increase of people suffering from various neural disorders, the need for brain-computer interfaces (BCIs) to regain sensory-motor or cognitive functions are expected to become acute in the coming decades. However, the existing BCIs can only control simple motions, e.g., grasping, and are far from realizing our vision to help paralyzed patients to walk again. This is due to the lack of a high-bandwidth wireless BCI, capable of supporting the recording from a large number of neurons with high spatial and temporal resolution, while having large spatial coverage, brain-wide. In IoN, we target to achieve a breakthrough in the ability to transfer data from intracortical recording devices, e.g., multi-electrode arrays, by developing a transcranial telemetry system that enables the efficient transfer at high data rate from such high channel count sensors (e.g., imec’s Neuropixels with 1000 channels). Most importantly, it will also fulfil the form factor required for minimally-invasive surgery, needed to minimize the surgical risk and the complications after insertion. Furthermore, IoN will significantly scale up brain-wide recordings, by introducing a new telemetry network that has the capacity to support 16 distributed recording nodes (enabling a total of 16,000 channels), which has never been demonstrated from any BCIs before. To reach these challenging targets, we propose i) a novel hybrid signal propagation method to achieve a 500Mbps data rate with a 10mm^2 implant area, 20× smaller than the state-of-the-art; ii) a completely new “spike-Aloha” protocol to maximize the network capacity, supporting 166× more channels. The technology developed in IoN will be an important transformational step to revolutionize the way neuroscientists and neurologists collect and process brain-wide neural data. By introducing this miniature, energy-efficient, and high-capacity wireless telemetry network, we want to help patients with disability to regain the quality of life.

Title : Cyber Security for Cross Domain Reliable Dependable Automated Systems. Goal : SECREDAS aims to develop and validate multi-domain architecting methodologies, reference architectures & components for autonomous systems, combining high security and privacy protection while preserving functional-safety and operational performance.