News : New computing paradigms, circuits and technologies, incl. quantum

January 01 2023

Cryo-CMOS electronics: Thermal and strain effects in FDSOI MOSFETs down to very low temperature

In the context of the development of the cryo-electronics, i.e. the extension of operation of digital or analog electronics to cryogenic temperatures, down to a few tens of mK, in particular for quantum applications, the aim of the post-doctoral project is to continue the effort of modeling and characterization of the FDSOI technology at low […] >>

January 01 2023

Design and fabrication of the magnetic control of 1.000 qubits arrays

Quantum computing is nowadays a strong field of research at CEA-LETI and in numerous institutes and companies around the world. In particular, RF magnetic fields allow to control the spin of silicon qubits, and pathway for large scale control is a real technological challenge. The bibliographic analysis and the studies already carried out will able […] >>

January 01 2023

DTCO analysis of MRAM for In/Near-Memory Computing

The energy cost associated to moving data across the memory hierarchy has become a limiting factor in modern computing systems. To mitigate this trend, novel computing architectures favoring a more local and parallel processing of the stored information are proposed, under the labels « Near/In-Memory Computing » or « Processing In Memory ». Substantial benefits […] >>

January 01 2023

Ferroelectric field effect transistor (FeMFET) for multi-bit per cell memories and in-memory computing applications

The recent discovery of ferroelectricity in hafnium oxide (HfO2) thin films generates a strong interest for ultra-low power non volatile memories, in which the information is stored by the orientation of the electric dipoles in this material. Among those memories, the ferroelectric field effect transistor (FeFET) is quite appealing to combine logic and memory operations […] >>

January 01 2023

Networks of stochastic magnetoresistive components for ultra-low-power cognitive computing

The automated resolution of cognitive tasks primarily relies on learning algorithms applied to neural networks which, when executed on standard architectures, lead to a power consumption several orders of magnitude larger than what the brain would require. This consumption can be drastically reduced by using hardware computing systems with an architecture inspired by biological or […] >>
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