PhD on High-Endurance Chalcogenide Memories for AI
Develop advanced characterization techniques to improve endurance in nanoscale memory devices for artificial intelligence.
Best for:Best for graduate students with a strong background in electrical engineering, semiconductor physics, or nanoscale electronic devices.
Overview
This PhD position at Université Grenoble Alpes, in collaboration with CEA LETI, focuses on exploring the degradation phenomena and electrical characteristics of chalcogenide-based memories, specifically Phase-Change Memory (PCM) and Threshold Change Memory (TCM). The candidate will work within multidisciplinary teams to study nanoscale devices and optimize programming protocols to extend endurance limits for high-performance artificial intelligence applications. The research involves close collaboration with another PhD student conducting advanced transmission electron microscopy (TEM) analyses to correlate electrical behavior with physico-chemical degradation mechanisms. The successful applicant will contribute to scientific publications, project deliverables, and innovative technological solutions.
What you'll do
- Perform electrical characterization of PCM and TCM devices to evaluate cycling-induced degradation.
- Develop and assess innovative programming protocols designed to extend device endurance limits.
- Propose and test engineering solutions to enhance the reliability and performance of next-generation memory technologies.
- Collaborate closely with peer researchers interpreting TEM results to link electrical performance with degradation mechanisms.
- Contribute to scientific reports, presentations, and publications in international conferences and journals.
Requirements
Degree required: Master of Science or equivalent degree in Microelectronics, Electrical Engineering, Applied Physics, or related field
Degree offered: PhD
Language: English; professional proficiency
Instruction: English
- Strong background in semiconductor device physics and electrical characterization
- Experience with nanoscale electronic or memory devices
- Analytical mindset with capability to interpret complex physical and electrical data
- Strong communication and collaborative skills for multidisciplinary teamwork
Funding & terms
How to apply
Deadline: 2026-11-30
Interested candidates should submit their application package including academic transcripts, a CV, and a motivation letter via the official portal or by contacting the listed supervisor directly.
- CV
- Cover letter
- Academic transcripts
Contact
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