PhD - Leuven | More than two weeks ago
Time-dependent dielectric breakdown (TDDB) of the thin oxide layer in a transistor is a reliability issue that has received a tremendous amount of attention in the past by many research groups. In imec, a solid expertise exists on the modeling, measurement and statistical analysis of TDDB. In the 2010s, the problem disappeared largely from the academic radar – especially in FEOL – as it was not considered to be the most stringent reliability issue, but in more recent technologies, TDDB has returned as an important FEOL reliability concern. This is due to the careful optimization of modern technologies using different metal gates combined with dielectric stacks consisting of various high-k components and delicate work function tuning treatments.
It was common practice to stress oxide layers at accelerated conditions and extrapolate the measured time-to-breakdown to operating conditions using an extrapolation model. At imec, however, fundamental TDDB research focused on modeling the physical principles that underpin the dielectric degradation and calculate the time-to-breakdown at any condition. In this way, a new practical method for determining TDDB reliability and benchmarking oxide quality is being developed.
However, some crucial aspects of TDDB are not yet understood, as for example the post-breakdown wear-out phase, while other aspects need to be thoroughly revisited, as for example the temperature dependence, the behavior of multi-layered structures, the behavior under AC stress conditions, the impact of electrode work functions and the interaction between TDDB and other reliability issues like Bias Temperature Instability (BTI) and hot carrier-induced degradation. The target of this PhD is to develop a practical physics-based model that can describe these phenomena.
The student will work in a team with dedicated reliability specialists and will have the opportunity to learn from and build upon the large existing knowledge in this research team. Furthermore, dedicated experimental techniques and specialized software have been developed over the years to measure, analyze, and interpret reliability data. Currently, we are also exploring physics-informed machine learning techniques to extract detailed information from previously uninterpretable complex data. Building on this solid knowledge base, the student will be able to immediately produce cutting-edge research results that can be published at top-level conferences and journals.
Specifically, in this PhD, the student will focus on the following aspects:
Required background: Engineering sciences or physics or equivalent
Type of work: 40% modeling 40% experimental work 20% literature
Supervisor: Clement Merckling
Daily advisor: Robin Degraeve
The reference code for this position is 2024-053. Mention this reference code on your application form.