/2D Materials integration in a CMOS flow to enable mass-manufacturable devices

2D Materials integration in a CMOS flow to enable mass-manufacturable devices

PhD - Leuven | More than two weeks ago

integrating 2D films in application relevant fabrication flows, focusing on electrical biosensors and/or photonic devices

The discovery and exploration of graphene and other 2D-materials has generated an enormous boost for both fundamental and device-oriented research, ranging from advanced scaling to photonics and sensor applications. Fantastic device properties have been demonstrated in high impact journal papers, ranging from ultrahigh mobility devices, high speed integrated photonic modulators, single molecule electrical devices & nanopores for DNA sequencing. Practical use of these materials, however, has been hampered by the difficulties in mass manufacturability of these devices. The main issues concern the large scale growth of high quality 2D films and their integration in CMOS compatible fabrication flows.

Currently these problems are largely overcome due to developments on growth and on layer transfer techniques enabling embedding 2D films in complicated semiconductor-based stacks. Hence, the time is ripe to take the next step and demonstrate those applications using flows that actually can be transferred to a manufacturing scheme. Characterization of the transferred film quality and correlated device performance will be important key metrics to determine the penetration of this technology in different markets.

In this PhD you will focus on integrating 2D films in application relevant fabrication flows, focusing on electrical biosensors and/or photonic devices. The work involves extensive material characterization, careful device design and optimization based on the available stack and intensive interactions with both the process integration team and the application engineers.



Required background: Electrical engineering, Physics, or related

Type of work: 15% literature, 55% experimental work, 30% Design & Simulations

Supervisor: Liesbet Lagae

Co-supervisor: Xavier Rottenberg

Daily advisor: Deniz Sabuncuoglu Tezcan

The reference code for this position is 2022-095. Mention this reference code on your application form.