Leuven | More than two weeks ago
To prevent defects caused by nanobubbles
In semiconductor manufacturing, the fight against defectivity is as old as the creation of the first transistor. Cleaning of metallic impurities and particles has always been necessary to achieve the high production yield leading to economic gains. In this PhD, a new potential source of defects will be studied. Bulk nanobubbles have a diameter of about 100 nm, are generated by the agitation of gas-containing water and can survive for months, while the prediction is less than one msec, based on the size. Moreover, the mechanism of stabilization of bulk nanobubbles is still under discussion.
Impurities have been considered as playing a role in the stabilization of bulk nanobubbles. Results were negative, but only a few impurities were tested. In our research at imec, impurities present in ultra-pure water (UPW) have been shown to participate in the generation of bulk nanobubbles. However, neither the impurity(ies) has(have) been identified, nor its(their) role (catalyst? component?).
The formation of drying marks by surface nanobubbles has been shown in the literature. Hence, surface defects are thought to be caused by the drying of adsorbed bulk nanobubbles. Bulk nanobubbles are negatively charged and will adsorb on positively charged surfaces. Also, drying of wafers is accompanied in the final step by the evaporation of a film of about one micron, forcing all impurities present in the rinsing water to deposit on the surface.
The goals of the proposed PhD are fourfold: (1) determine the role played by some representative impurities present in the UPW used in semiconductor manufacturing, (2) study the defects caused by nanobubbles on different types of surfaces, (3) develop solutions for the annihilation of nanobubbles, and (4) study the influence of bulk nanobubbles on the properties of water that are relevant for wet etching and cleaning.
Required background: physical chemistry, surface science, materials science, or nano-engineering
Type of work: 10% literature study, 60% processing and data analysis, 30% characterization
Supervisor: Stefan De Gendt
Co-supervisor: Guy Vereecke
Daily advisor: Guy Vereecke, Alina Arslanova
The reference code for this position is 2025-028. Mention this reference code on your application form.