Date of Award

Fall 2011

Degree Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

Department

Electrical and Computer Engineering

Supervisor

M. Jamal Deen

Co-Supervisor

Ponnambalam Selvaganapathy, Xun Li

Language

English

Committee Member

Ponnambalam Selvaganapathy, Xun Li

Abstract

Achieving control over the construction and operation of microfabricated label-free DNA biosensors would be a big leap in the quest for highly reliable clinical laboratory tests. Reliable outcomes of critical medical tests mean less need for repetitions and earlier isolation of outbreaks. Nanotechnology has lent itself well to this purpose, with a plethora of work that attempt to produce highly sensitive nano-biosensors for detection of DNA strands. The problem of achieving a repeatable outcome is crude at best. Additionally, the mechanism of sensing in label-free Field-Effect based DNA sensors is still a matter of dispute. Simulation of the sensors using physical models can shed light into these mechanisms and help answer this question. Computational calculations can also allow designers to assess the importance of several parameters involved in the fabrication.

In this thesis, the problem of modeling FET-based DNA hybridization sensors (named BioFET) is approached. Using the Finite-Element Method, a scalable model for the BioFET is produced and solved in 3D. The results are compared to an earlier work and we find that higher dimension physical modeling is essential for more realistic results. Additionally, we present a model for the impedance of the BioFET which allows the calculation of parasitic components that can contaminate the impedance measurements.

The issue of variations in the sensed signal from the BioFET is addressed by performing hybrid Finite-Element/Monte Carlo simulations on the conformation of single-stranded DNA. From electrostatic considerations alone, it is concluded that the change of conformation upon hybridization is a main contributor to the induced signal. We also simulate the positional variations of the DNA molecules on the sensitive surface. This computation yields an estimate for the amount of variation in the sensed signal due to the random placement of DNA molecules, and an estimate for the total signal-to-noise ratio is deduced.

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