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Electrical characterization of memristors for neural networks (AI)
Resistive memories have emerged as a pivotal advancement in modern electronics, offering remarkable advantages in energy efficiency, scalability, and non volatile operation. However, TiO2 based OxRAM devices still face key challenges, particularly regarding circuit level variability and the limited number of demonstrations in realistic application settings. This project addresses these gaps by thoroughly characterizing OxRAM switching dynamics, assessing their integration within analog and digital circuits, and developing automated Python based testing tools on the LOTUS neuromorphic platform. Together, these efforts will help establish the viability of OxRAM based analog modules for neuromorphic processing and next generation embedded AI systems.
Faculty Supervisor:
Malak Kanso
Student:
Partner:
Université Evry Paris-Saclay
Discipline:
Engineering
Sector:
Education
University:
Université de Sherbrooke
Program:
Globalink Research Award
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Mitacs is funded by the Government of Canada, the Government of Alberta, the Government of British Columbia, Research Manitoba, the Government of New Brunswick, the Government of Newfoundland and Labrador, the Government of Nova Scotia, the Government of Ontario, Innovation PEI, the Government of Quebec, the Government of Saskatchewan, and the Government of Yukon.