Optimization of the design of experimental (DoE) simulations of Ion-sensitive Field Effect Transistors

Dhar, Rakshita Pritamsingh (2023) Optimization of the design of experimental (DoE) simulations of Ion-sensitive Field Effect Transistors. PhD thesis, University of Glasgow.

Abstract

To make personalised medicines for cancer treatment we need to detect a specific antigen with antibodies. Studies are being carried out for these antigen-antibody interactions. This can be done with the help of nano biosensors. Nano biosensors are nano-scaled biosensors used to detect specific biomolecules in the sample. The sample can be present in a solution. This thesis is a radical vision of imitating the electrochemical synthesis of peptide [1] receptors by a programmable in-situ protein (target molecules) detection with nano-functionalised FinFET sensors by simulations.

In order to do so, we have started first by simulation of an Ion-sensing field effect tran sistor(ISFET) where we detect the ions to determine the absolute pH of the solution. The ions/target molecules are protons in this case. The receptors are further modified to increase the surface complexity. This helps us understand better the solution/surface interactions which will help us in future to detect a specific protein.

This thesis aims to achieve a technological breakthrough of the first fully programmable ion screening simulation tool. This simulation tool will come as a faster, cheaper, and more efficient technology. The simulation of this is performed by using MATLAB and Synopsys Sentaurus TCAD software. A study of different types of models for the bio-interface along with an analysis of output characteristics obtained from simulation is presented in this report.

• Item Type: Thesis (PhD)
• Qualification Level: Doctoral
• Subjects: T Technology > T Technology (General)
• Colleges/Schools: College of Science and Engineering > School of Engineering > Electronics and Nanoscale Engineering
• Supervisor's Name: Georgiev, Professor Vihar
• Date of Award: 2023
• Depositing User: Theses Team
• Unique ID: glathesis:2023-83715
• Copyright: Copyright of this thesis is held by the author.
• Date Deposited: 06 Jul 2023 09:13
• Last Modified: 07 Jul 2023 14:50
• Thesis DOI: 10.5525/gla.thesis.83715
• URI: https://theses.gla.ac.uk/id/eprint/83715