Jörg SchülerHighTech Startbahn
Innovation Results and Future Concepts of Research Cluster FAST
In the "fast" project, the aim is to achieve a technological breakthrough through real-time. Real-time means that we make technology so fast that no delay is noticed on the technology side. This is the enabler for improved and new applications in industry, traffic, health and communication technology. For almost all applications that deal directly with humans, a latency of 1ms is sufficient. However, for applications e.g. in the field of automation between machines, even shorter times may be required. "fast" relies on a holistic approach that considers and examines the entire functional chain of the systems in order to develop optimal innovations for the respective use cases. This is done directly with the industry to ensure the shortest possible innovation times to product launch. Decisive for success are novel concepts that reliably minimize the reaction times of the systems. In this respect, excellent research results have been achieved in "fast", which play a central role for the strategy and future exploitation, have massively improved the state of the art, generate market advantages, create jobs and ultimately increase the quality of human life. We would be pleased to present these to you in the lecture.
Special interest is paid to the sensor’s interface. Fraunhofer EAS, located in Dresden, develops in the project a sensor analog frontend ASIC for environmental sensing. It is deployed in a Smart City project demonstrator to measure several key parameters of urban air quality. The Frontend-ASIC converts multi-physical sensor signals and combines ultra-low power consumption with improved hardware flexibility. The reuse of analog design data across different CMOS technologies is greatly improved by EAS Intelligent IP (IIP) flow.
In the last part an outlook to current trends in sensor electronics is presented: From the simple Always-Listening device the transition has begun to truly intelligent sensing electronics, which are aware of data context, further reducing energy demand of sensor networks. Additionally, current research activities in adaptive self-learning devices are presented.