3D chemical analysis of downscaled ePCM devices for sub-18 nm technology nodes using STEM-EDX tomography and machine learning tools

Job description: The context of this PhD is the recent progress of Phase-Change Memory technology in the embedded applications (ePCM). The ultimate scaling of ePCM for sub-18nm nodes poses many challenges not only in fabrication, but also in the physico-chemical characterization of these devices. The aim of the project is to study the 3D chemical segregation/crystallization phenomena in new PCM alloys integrated into planar and vertical ePCM scaled devices, using electron tomography in STEM-EDX (and 4D-STEM) mode. Given the extreme downscaling and the complex geometry of the devices, the focus will be on optimizing experimental conditions and applying machine learning and deep learning techniques to improve the quality and reliability of the obtained 3D results. A correlation with the device electrical behavior will be carried out to better understand the phenomena behind failures after endurance and after data loss at high temperatures. A probe-corrected Cold-FEG NeoARM TEM (60kV-200kV) will be used for the tomographic data acquisition. It is equipped with two large solid angle SSD detectors (JEOL Centurio), a CEOS Energy-Filtering and Imaging Device (CEFID) and a Timepix3 direct electron camera. The candidate will also have access to in-house Python codes as well as to the computing resources needed to carry out the spectral and tomographic data analysis.

Your profile: Master degree in Physics/Materials Science with a strong background in image analysis and/or electron microscopy