Abstract
In this paper, analytical modeling of a Dielectric Modulated Double Gate Field Effect Transistor (DM-DGFET) for biosensing application is presented with extensive data analysis. Firstly, the size of the nanogaps and arrangements of biomolecules in those gaps are optimized with respect to the sensitivity of the above sensor. The optimized DM-DGFET is next analyzed on the basis of its modeling and simulation. This paper addresses novel issues arising from arrangements of biomolecules, especially from practical point of view. Effect of probe placement due to steric hindrance and random nature of biomolecules, are also considered. The capacitances associated with the nanogaps occupied by biomolecules, following various arrangements, are modeled. Expressions of the threshold voltage, drain current and its sensitivity in terms of variations are also derived using the capacitance model. A comparative study of the proposed and the existing architectures is made. The influence of process variation on the sensitivity of the sensor is also studied. The results from the proposed analytical model are validated with the simulated data obtained from TCAD device simulator. In conclusion, the proposed DM-DGFET based biosensor architecture will emerge as an optimal model, very useful for the study on this field in future.
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We would like to thank the Department of ETCE, Jadavpur University for their constant support for providing the access of the laboratory.
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Rahul Das: Conceptualization, Methodology, Software, Data curation, Writing, Reviewing. Ankush Chattopadhyay: Software, Writing-Original draft preparation, Methodology, Reviewing and Editing. Manash Chanda: Conceptualization, Reviewing and Editing. Chandan K. Sarkar: Supervision. Chayanika Bose: Conceptualization, Methodology, Supervision, Correction of manuscript, Reviewing and Editing.
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Das, R., Chattopadhyay, A., Chanda, M. et al. Analytical Modeling of Sensitivity Parameters Influenced by Practically Feasible Arrangement of Bio-Molecules in Dielectric Modulated FET Biosensor. Silicon 14, 9417–9430 (2022). https://doi.org/10.1007/s12633-021-01617-z
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DOI: https://doi.org/10.1007/s12633-021-01617-z