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Analytical Investigation of a Split Double Gate Graded Channel Field Effect Transistor for Biosensing Applications

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Abstract

A field-effect transistor (FET) based biosensor with nano-cavity on a single side has high sensing ability, improved binding probability, and better structural stability than a nanogap biosensor. However, the analyte size and the presence of nano-cavity result in high threshold voltage and lower driving capability. This paper presents a double gate graded channel FET with stacked dielectrics for label-free biosensing applications. Two symmetrical nano-cavities have been created in the gate oxides to bind the analytes. The primary analytes used in the analysis are DNA and avian influenza antibody (AI-ab). Suitable receptors for DNA and AI-ab have been used to enhance the device’s selectivity. The analysis is done based on the surface potential and threshold voltage model. Additionally, variations in the electric field along the device channel have been modeled in order to gain a better understanding of the device’s characteristics. In the study, the threshold voltage shift due to the analyte’s binding is considered the sensing criterion. The results of the analysis have shown the superior sensing and driving capacity of the proposed device. The model results have been verified by using TCAD simulated data. Hence, the proposed device can be considered in the development of a portable, low cost FET device for biosensing applications with high sensing ability.

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Authors and Affiliations

Authors

Contributions

Both authors contributed to the completion of the work. The analytical modeling and simulation were performed by Khuraijam Nelson Singh. Preparation of the first draft of the manuscript and analysis of the data were performed by Khuraijam Nelson Singh and Pranab Kishore Dutta. Both the authors commented on previous versions of the manuscript. Both authors read and approved the final manuscript.

Corresponding author

Correspondence to Khuraijam Nelson Singh.

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Appendices

Appendix A

$$ \begin{array}{@{}rcl@{}} W_{1}&=&\lambda_{3}e^{(\lambda_{2}L_{2}-\lambda_{3}L_{3})}+e^{\lambda_{2}L_{2}}(\lambda_{3}+\lambda_{2})\sinh(\lambda_{3}L_{3}) \end{array} $$
(32)
$$ \begin{array}{@{}rcl@{}} W_{2}&=&\lambda_{3}e^{-(\lambda_{2}L_{2}+\lambda_{3}L_{3})}+e^{-\lambda_{2}L_{2}}(\lambda_{3}-\lambda_{2})\sinh(\lambda_{3}L_{3}) \end{array} $$
(33)
$$ \begin{array}{@{}rcl@{}} W_{3}&=&\lambda_{3}\{ V_{bi,2}+V_{ds}+P_{3}+(P_{2}-P_{3})\cosh(\lambda_{3}L_{3})\} \end{array} $$
(34)
$$ \begin{array}{@{}rcl@{}} W_{4}&=&e^{\lambda_{1}L_{1}}\{(\lambda_{2}+\lambda_{1})(W_{2}-W_{1})-2\lambda_{2}W_{2}\} \end{array} $$
(35)
$$ \begin{array}{@{}rcl@{}} W_{5}&=&e^{-\lambda_{1}L_{1}}\{(\lambda_{2}-\lambda_{1})(W_{2}-W_{1})-2\lambda_{2}W_{2}\} \end{array} $$
(36)
$$ \begin{array}{@{}rcl@{}} W_{6}&=&\lambda_{2}(P_{2}-P_{1})(W_{1}+W_{2})-2\lambda_{2}W_{3}-(V_{bi,1}+P_{1})W_{5} \end{array} $$
(37)
$$ \begin{array}{@{}rcl@{}} W_{7}&=&\lambda_{2}(P_{2}-P_{1})(W_{1}+W_{2})-2\lambda_{2}W_{3}-(V_{bi,1}+P_{1})W_{4} \end{array} $$
(38)
$$ \begin{array}{@{}rcl@{}} W_{8}&=&\lambda_{1}\cosh(\lambda_{1}L_{1})-\lambda_{2}\sinh(\lambda_{1}L_{1}) \end{array} $$
(39)
$$ \begin{array}{@{}rcl@{}} W_{9}&=&\lambda_{1}\cosh(\lambda_{1}L_{1})+\lambda_{2}\sinh(\lambda_{1}L_{1}) \end{array} $$
(40)
$$ \begin{array}{@{}rcl@{}} W_{10}&=&(V_{bi,1}+P_{1})\lambda_{1}W_{2}-(P_{1}-P_{2})\lambda_{1}W_{2}\cosh(\lambda_{1}L_{1}) \\ &&-W_{9}W_{3} \end{array} $$
(41)
$$ \begin{array}{@{}rcl@{}} W_{11}&=&(V_{bi,1}+P_{1})\lambda_{1}W_{1}-(P_{1}-P_{2}\lambda_{1}W_{1}\cosh(\lambda_{1}L_{1})\\ &&-W_{8}W_{3} \end{array} $$
(42)
$$ \begin{array}{@{}rcl@{}} W_{12}&=&(\lambda_{3}-\lambda_{2})(W_{1}e^{-\lambda_{2}L_{2}}-W_{2}e^{\lambda_{2}L_{2}})+2\lambda_{2}e^{-\lambda_{2}L_{2}}W_{1} \end{array} $$
(43)
$$ \begin{array}{@{}rcl@{}} W_{13}&=&(\lambda_{3}+\lambda_{2})(W_{1}e^{-\lambda_{2}L_{2}}-W_{2}e^{\lambda_{2}L_{2}})-2\lambda_{2}e^{-\lambda_{2}L_{2}}W_{1} \end{array} $$
(44)
$$ \begin{array}{@{}rcl@{}} W_{14}&=&(V_{bi,2}+V_{ds}+P_{3})W_{13}+(P_{3}-P_{2})\lambda_{2}(W_{2}e^{\lambda_{2}L_{2}} \\ &&+W_{1}e^{-\lambda_{2}L_{2}})e^{-\lambda_{3}L_{3}}+2\lambda_{2}W_{3}e^{-\lambda_{3}L_{3}} \end{array} $$
(45)
$$ \begin{array}{@{}rcl@{}} W_{15}&=&(V_{bi,2}+V_{ds}+P_{3})W_{12}+(P_{3}-P_{2})\lambda_{2}(W_{2}e^{\lambda_{2}L_{2}}\\ &&+W_{1}e^{-\lambda_{2}L_{2}})e^{\lambda_{3}L_{3}}-2\lambda_{2}W_{3}e^{\lambda_{3}L_{3}} \end{array} $$
(46)

Appendix B

$$ \begin{array}{@{}rcl@{}} V_{1}&=&(2\lambda_{3}\lambda_{2}+W_{5})(2\lambda_{3}\lambda_{2}+W_{4})+\left\{\frac{W_{5}-W_{4}}{2} \right\}^{2} \end{array} $$
(47)
$$ \begin{array}{@{}rcl@{}} V_{2}&=&W_{6}(2\lambda_{3}\lambda_{2}+W_{4})+W_{7}(2\lambda_{3}\lambda_{2}+W_{5})\\ &&+\left\{\frac{(W_{5}-W_{4})^{2}(2\phi_{1}-P_{1})}{2}\right\} \end{array} $$
(48)
$$ \begin{array}{@{}rcl@{}} V_{3}&=&W_{7}W_{6}+\left\{\frac{(W_{5}-W_{4})(2\phi_{1}-P_{1})}{2}\right\}^{2} \end{array} $$
(49)
$$ \begin{array}{@{}rcl@{}} V_{4}&=&{\lambda_{1}^{2}}(W_{2}-W_{9})(W_{1}-W_{8})+\left( \frac{W_{9}W_{1}-W_{8}W_{2}}{2}\right)^{2} \end{array} $$
(50)
$$ \begin{array}{@{}rcl@{}} V_{5}&=&\left\{\frac{(W_{9}W_{1}-W_{8}W_{2})^{2}(2\phi_{F,2}-P_{2})}{2}\right\}\\ &&+\lambda_{1}\{W_{10}(W_{1}-W_{8})+W_{11}(W_{2}-W_{9})\} \end{array} $$
(51)
$$ \begin{array}{@{}rcl@{}} V_{6}&=&W_{11}W_{10}+\left\{\frac{(W_{9}W_{1}-W_{8}W_{2})(2\phi_{F,2}-P_{2})}{2}\right\}^{2} \end{array} $$
(52)
$$ \begin{array}{@{}rcl@{}} V_{7}&=&(W_{13}+2\lambda_{3}\lambda_{2}e^{-\lambda_{3}L_{3}})+(W_{11}-2\lambda_{3}\lambda_{2}e^{\lambda_{3}L_{3}}) \end{array} $$
(53)
$$ \begin{array}{@{}rcl@{}} V_{8}&=&W_{14}(2\lambda_{3}\lambda_{2}e^{\lambda_{3}L_{3}}-W_{11})-W_{15}(W_{13}+2\lambda_{3}\lambda_{2}e^{-\lambda_{3}L_{3}})\\ &&-\frac{(W_{12}e^{\lambda_{3}L_{3}}+W_{11}e^{-\lambda_{3}L_{3}})^{2}(2\phi_{F,3}-P_{3})}{2} \end{array} $$
(54)
$$ \begin{array}{@{}rcl@{}} V_{9}&=&-\left\{\frac{(W_{12}e^{\lambda_{3}L_{3}}+W_{11}e^{-\lambda_{3}L_{3}})(2\phi_{F,3}-P_{3})}{2}\right\}^{2}\\ &&+W_{15}W_{14} \end{array} $$
(55)

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Singh, K.N., Dutta, P.K. Analytical Investigation of a Split Double Gate Graded Channel Field Effect Transistor for Biosensing Applications. Silicon 14, 11303–11313 (2022). https://doi.org/10.1007/s12633-022-01774-9

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