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Towards "Zero-Power" Signal Conditioning IPs for IoT

Presentation held at 14th Silicon Saxony Day, 18. Juni 2019, Conference Center Dresden Airport
: Jotschke, Marcel; Prautsch, Benjamin

Präsentation urn:nbn:de:0011-n-5497108 (3.4 MByte PDF)
MD5 Fingerprint: 9d7e082f81bb1dd26cf47fe0851f9c46
Erstellt am: 6.7.2019

2019, 29 Folien
Silicon Saxony Day <14, 2019, Dresden>
Fraunhofer-Gesellschaft FhG
Towards Zero Power Electronics
Vortrag, Elektronische Publikation
Fraunhofer IIS, Institutsteil Entwurfsautomatisierung (EAS) ()

Internet of Things (IoT) is on the way to widespread usage. A large amount of connected Sensor Nodes (SNs) collect and process environmental data, e. g. in the Smart City vision. The problem: The energy demand of these networks grows as well, creating power and routing overhead for machines, vehicles and infrastructure. Autonomous sensor nodes are strongly desired, but they are restricted in life-time and performance. Till now, few application-specific solutions, but no sustainable generic solution was presented. The Fraunhofer group develops in project Towards Zero Power Electronics (ZEPOWEL) an autonomous Wake-Up RF-SN solution, supported by energy harvesters. The hardware is suitable for many upcoming IoT applications while providing trend-setting energy efficiency. The vision is the Zero-Power device, which is powered solely from harvested energy. 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.