PhD - Leuven | More than two weeks ago
Nevertheless, the application of ASD for CFET fabrication is currently hampered as today only a limited number of materials can be deposited by ASD. In addition, the inherent surface dependence of ALD and CVD processes is rarely sufficient, as high selectivity is needed to enable high yield in device fabrication. A better understanding in the surface dependence of ALD and CVD processes and the role of the precursors is essential to expand the material combinations accessible by ASD, as well as to improve selectivity.
The general aim of this PhD project is to generate insight in the selectivity of ALD and CVD processes and use that to design novel ASD processes for the fabrication of CFET devices. A first research objective is to generate detailed insight into the surface chemistry during ALD and CVD for materials combinations that are relevant for future CFET integration flows. We will investigate the surface composition of the materials and study their impact on the growth and nucleation behavior during deposition. As ALD and CVD rely on surface reactions of gas phase precursors, the deposition is sensitive to the substrate surface, which can be chemically modified to either enable or prevent growth. Surface treatments will therefore be investigated to modify the surface termination and as such manipulate the surface reactivity to enable or prevent deposition. A second research objective is to apply this insight to design ASD processes, and to study the ASD mechanism in nanoscale patterns, where the growth mechanism can be different than for regular thin film deposition. In addition, the selectivity might be affected by the patterning process due to changes in surface composition. We will leverage imec’s 300mm production line and advanced node CMOS technologies to gain access to patterned structures with topography and dimensions down to tens of nanometers in order to industrially relevant research.
Required background: Chemistry, Physics, Materials, Nanotechnology
Type of work: 10% literature study, 90% experimental work
Supervisor: Annelies Delabie
Daily advisor: Annelies Delabie
The reference code for this position is 2022-049. Mention this reference code on your application form.