The team is dedicated to the evaluation of the benefits of using magnetic devices in Integrated Circuits (ICs). It is expected that integrating non-volatility in ICs could contribute to push forward the incoming limits in the microelectronics scaling. This work includes integrating the magnetic devices in standard design tools, design hybrid circuits and evaluate their performance for various applications. The unique combination of advantages of spintronics devices (non-volatility associated with high speed and endurance, analogue capabilities, well controlled stochastic behavior…) allows intrinsically mixing the memory and logic functionalities (in Memory Computing). This opens the way towards new computing paradigms, beyond the standard Von-Newman architecture of computing systems. The most interesting applications addressed in the team are described below.
Hybrid CMOS/Magnetic design flow
Designing hybrid circuits requires integrating the magnetic devices in the standard design flow of microelectronics. This includes compacts models for electrical simulations, technology files including the magnetic back-end and libraries of Standard Cells for digital design.
Low-power logic circuits
One issue related to microelectronics scaling is the increasing standby power, due to leakage currents. Introducing non-volatility in circuits allows easing the power gating technique, which consists in cutting-off the power supply of inactive blocks to save leakage.
Spiking Neural Networks are seen as a Key building block for strongly improving the energy efficiency of current AI applications and opening up new possibilities (in terms of unsupervised learning, recurrent networks, probabilistic inference, etc.). The scientific challenges to be tackled are the following: the first one is to define power-constrained learning and inference algorithms (online, supervised, unsupervised, probabilistic, etc.). The second one is to design a scalable and flexible SNN architecture, adaptable to the different above-mentioned algorithms, and fabricate that circuit in hybrid nanoscale CMOS and NVM technology, enabling very dense synaptic density. The last objective is to derive a principled toolchain for the algorithm, design, development, and integration of spiking neural networks for future adoption in industrial health and automotive embedded applications.
IC Reliability : Hardware security
While STT MRAM can be beneficial for hardware security (taking advantage of its stochastic behavior for cryptography for instance), it also presents some specific failures mechanisms that has to be studied to take the appropriate countermeasures.
IC Reliability : Radiation hardening
The intrinsic hardness to radiations of the magnetic devices make them a good candidate to be embedded in circuits for space applications. It can be advantageously combined with other hardening technologies or design techniques targeting space applications.
- NV-APROC, ANR (2019-2023) – MRAM-based Non-volatile Asynchronous Processor
- MISTRAL, ANR (2019-2023) – MRAM/CMOS Hybridization to secure cryptographic algorithms
- SPINBRAIN (2020-2022) – Spintronic-based Neural Network
- HANS, UGA (2019-2022)
- CARMEM, (2021-2024) – Modèles de Caractérisation par Apprentissage pour la Qualité des Technologies de Mémoires Émergentes
- Christophe LAYER : Research scientist
- François DUHEM: Research scientist
- Ghislain TAKAM TCHENDJOU (2020-2021)
- Pierre VANHAUWAERT (2014-2017)
- Eldar ZIANBETOV (2014-2017)
- Kotb JABEUR (2013-2017)
- Virgile JAVERLIAC (2013-2014)
- Fabrice BERNARD-GRANGER (2013-2014)
- Yun YANG (2012-2013)
- Abdelilah MEJDOUBI (2010-2012)
- Odilia COI (supervised by G. Di Pendina, D. Dangla and L. Torres) (2018-2021)
- Antoine HERAUD (supervised by Lorena ANGHEL and Alexandre VALENTIAN) (2020-2022)
- Etienne BECLE (supervised by Lorena ANGHEL, G. PRENAT and I.L. Prejbeanu) (2019-2021)
- Mounia KHARBOUCHE (supervised by G. Di Pendina, R. Wacquez and J.M. Portal) (2016-2019)
- Rana ALHALABI (supervised by G. Di Pendina, E. Nowak and L. Prejbeanu) (2016-2019)
- Jeremy LOPES (supervised by G. Di Pendina, E. Beigne, D. Dangla and L. Torres) (2014-2017)
- Erya DENG (supervised by G. Prenat and L. Anghel) (2014-2017)
- Olivier GONCALVES (supervised by G. Prenat and B. Dieny) (2009-2012)
- Wei GUO (supervised by G. Prenat and B. Dieny) (2006 – 2010)
- Mourad El BARAJI (supervised by G. Prenat and B. Dieny) (2007-2009)
- Stephane GROS (2013-2014)
- Pierre PAOLI (2013-2014)
- GREAT, H2020 (2016-2019)
- MASTA, ANR (2016-2019)
- ELECSPIN, ANR (2016-2020) – Electric-filed control of spin-based phenomena
- NOVELASIC, CEA-nanosciences (2015)
- MAD, CEA internal (2014-2018)
- SPOT, H2020 (2012-2015)
- MARS, ANR (2012-2015)
- DIPMEM, ANR (2012-2015)
- HYMAGINE, ERC Advanced grant (2010-2015)
- Toplink Innovation
- University of Montpellier
- University of Brasov
- University of Aix-Marseille
- CEA Tech (Gardanne)
- EMSE (Gardanne)
- Tiempo Secure
- Dolphin Integration
- Thales TRT
- University of Newcastle
- EM Marin
- Seminar – Spin waves as state variables for logic and analog circuits (March 24th, 2022)
On Monday, June 13th at 14:00 Thibaut Devolder from C2N, CNRS, Univ. Paris-Saclay will give us a seminar entitled: Spin waves as state variables for logic and analog circuits Place : CEA Bat. 10.05 Room 445 (limited ...
- Masters thesis projects for Spring 2022 (September 17th, 2021)
You find here the list of proposals for Master-2 internships to take place at Spintec during Spring 2022. In most cases, these internships are intended to be suitable for a longer-term PhD work. Interested Master-1 ...
- Heavy ion irradiation effects on advanced Perpendicular Anisotropy Spin-Transfer Torque Magnetic Tunnel Junction (September 10th, 2021)
This study investigates heavy-ion irradiation effects on Perpendicular Magnetic Anisotropy Spin-Transfer Torque Magnetic Tunnel Junction devices (p-STT-MTJs). The radiative campaign took place at the Cyclotron Resource Centre of Université Catholique de Louvain (UCL). Designers of space ...
- NeuSPIN – An ANR project (June 24th, 2021)
NeuSPIN stands for Design of a reliable edge neuromorphic system based on spintronics for Green AI. Current computing architectures with separate processing and memory blocks are not ideal for energy-efficient learning and inference processing for ...
- Highlights of SPINTEC research in 2020 (December 10th, 2020)
The research highlights of SPINTEC over the year 2020 have been put together, and are available to download: https://www.spintec.fr/spintec-annual-booklets. This booklet contains the key facts of the lab over the period (contracts, new staff etc.), the ...