< Back to previous page

Project

Modeling of hybrid nanofluidic-nanoelectronic devices for single-molecule biosensing

The continuous optimization of the metal-oxide-semiconductor field-effect transistor (MOSFET) since the mid-60s has enabled ultra-scaled devices. This nano-scaling of MOSFETs has primarily benefited the field of computing, but is also expected to benefit the interdisciplinary field of biosensing. While biosensing, and in particular DNA sequencing, has been done successfully by ion current sensing through nanopores, a nanopore FET has been recently proposed as an alternative design. The detection of molecular motion through a nanopore inside a FET based on the FET's electrical characteristics is expected to solve multiple challenges, by offering larger signals, higher bandwidth, denser integration and parallel sensing. This project explores the optimal nanopore FET configuration through modeling, while connecting closely with experimental input from FET experts and from molecular dynamics experts. Modeling efforts are ground-breaking as a solver platform, including both semiconductor drift-diffusion equations as well as Nernst-Planck and Navier-Stokes equations for liquids, is virtually non-existing. A prototype design has been established with OpenFOAM, an open-source solver platform. This work will optimize this single-molecule biosensing FET design, based on physical insight in the performance. Next to structural modifications, the optimization will include an implementation of the noise of the biosensor, such that the optimal operating regime can be determined. This thesis will also improve the relevance of the predictions by implementing (available) molecular models into the solver platform. The ongoing prototype development in world-class 300mm semiconductor processing line and state-of-the-art laboratories will complement the topic of this PhD.

Date:26 Aug 2021 →  22 Apr 2022
Keywords:Modeling, nanofluidic, nanoelectronic, single-molecule biosensing
Disciplines:Nanobiotechnology
Project type:PhD project