/Advanced FET-based Biomolecule Sensing

Advanced FET-based Biomolecule Sensing

Leuven | More than two weeks ago

Explore biomolecular sensing leveraging cutting-edge nanotechnology.

As genome analysis is quickly revolutionizing the medical world, researchers are turning to the next step in large-scale analysis of biological systems.
Imec believes that an important innovation lies in large-scale integration of nanoscale transistors for analyzing biological systems. Indeed, a bio-electronic sensor chip could provide for massive parallelization and deliver a more complete view of a biological system at a reasonable cost. At present, several challenges still need to be tackled to achieve such a large-scale bio-electronic sensor chip.

In this PhD, we will investigate which electronic sensors are most suited to detect biological molecules in electrolytic environments. Computer simulation of various electronic sensors is a powerful tool to screen different sensor architectures and to predict their relative performance. This allows the PhD student to predict the most highly-performing sensor architectures. Modeling and theoretical insight will be corroborated by experiment on cutting-edge sensor devices made in imec’s nanomanufacturing facilities. We will explore innovative sensing techniques to enhance sensor performance beyond the state-of-the-art. In-depth sensor experiments will undoubtedly provide a wealth of information and exciting new insights in the field.


Fig.1 : Artist’s impression of a biosensor (nanowire FET) used to read DNA molecules in an electrolyte solution. The charged biomolecule binds close to the channel dielectric and modulates the current inside the nanowire.


Required background: Master Electrical Engineering or Nanotechnology or Bionanotechnology, profound interest in semiconductor devices, TCAD and FEM simulations

Type of work: Experiment, simulations and sensor design

Supervisor: Pol Van Dorpe

Co-Supervisor: Koen Martens

Daily advisor: Sybren Santermans

The reference code for this position is 2024-133. Mention this reference code on your application form.

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