PhD on AI Methodologies for Defect Impact Analysis in SiC Power Devices

This PhD position offers a funded research opportunity within the Semiconductor Power Components Laboratory (LETI) in France, focusing on silicon carbide (SiC) power devices. The doctoral researcher will build and deploy specialized AI tools to analyze the relationship between material crystal defects and electrical performance in diodes and power MOSFETs. Responsibilities include training machine learning models on existing electrical and defect-mapping datasets, and subsequently applying these frameworks to newly manufactured component batches. The successful candidate will work closely with industrial partners and characterization teams, contributing methodologies that can potentially extend to other wide bandgap materials.

• Design and implement specialized AI and data analysis tools tailored to the requirements of the SiC research team
• Train machine learning models using pre-existing datasets containing electrical data and defect mappings from SiC diode batches
• Apply developed methodologies to evaluate new manufactured and characterized semiconductor device batches
• Integrate diverse data inputs including defectivity images from clean room fabrication and advanced material characterizations
• Collaborate with internal facilities and external partners such as Line Pilot WBG and Soitec