Circuits, Devices and System Integration
since 2015

Research topics

photo CDSI

The Circuits, Devices and System Integration (CDSI) axis is involved in the design, fabrication and test of integrated devices, circuits and systems. The main driver of the activity is focused on the research in energy consumption and in miniaturized devices and systems. Results of this research are applicable for portable and autonomous systems, which usually require low-power consumption, miniaturization, robustness and security. Indeed, our digital society, which offers large communication facilities to people, tends to enlarge the communication to machines and small communicating objects such as wireless sensor nodes (Internet of Things). This will multiply by several orders of magnitude the amount of energy consumed by the digital objects and will push the industry to foster new approach for mitigating the power consumption. In this context, the CIS (Concurrent Integrated Systems) and MNS (Micro and Nano Systems) teams developed new techniques based on asynchronous circuitry, non-uniform sampling schemes, energy harvesting and low-power sensors and actuators.

Last publications

Fesquet L., Decoudu Y., Iga R., Ferreira De Paiva Leite T., Rolloff O., Diallo M., Possamai Bastos R., Morin-Allory K., Engels S., A Distributed Body-Biasing Strategy for Asynchronous Circuits, 27th IFIP/IEEE International Conference on Very Large Scale Integration (VLSI-SoC 2019), Cuzco, PERU, 2019
Bonnaud O., Fesquet L., Microelectronics at the heart of the digital society: technological and training challenges, 34th SBMicro – Symposium on Microelectronics and Devices, Sao Paolo, BRAZIL, 2019
Aquino Guazzelli R., Garay Trindade M., Fesquet L., Possamai Bastos R., Learning-Based Reliability Assessment Method for Detection of Permanent Faults in Clockless Circuits, 30th European Symposium on Reliability of Electron Devices, Failure Physics and Analysis (ESREF 2019), Toulouse, FRANCE, 2019
Kalsing A., Power-Intent Management During RTL Optimizations, These de Doctorat, 2019
Garay Trindade M., Coelho A., Valadares C., Andreoni Camponogara Viera R., Rey S., Cheymol B., Baylac M., Velazco R., Possamai Bastos R., Assessment of a Hardware-Implemented Machine Learning Technique under Neutron Irradiation, IEEE Transactions on Nuclear Science, Ed. IEEE, Vol. , 2019
Annual activity report