A detailed investigation of dielectric-modulated dual-gate TMD FET based label-free biosensor via analytical modelling

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Abstract

In this work, an analytical model is developed for DM-DG-TMD-FET- based Biosensor including Fringing-field effects. The Analytical model has been developed for two different Device structures, namely Device structure-1 (without a gate above the nano-cavity) and Device structure-2 (with a gate above the nano-cavity) based on modulation of the dielectric constant of biomolecules in the nano-cavity region. The proposed model has been validated against both numerical quantum simulation results with the help of a few fitting parameters and it also agrees with the 2-dimensional numeric simulator SILVACO TCAD used in this work. The presence/absence of biomolecules has been detected by the metric of threshold voltage sensitivity SVth and drain current Id for the neutral as well as charged biomolecules. Sensitivities of partially filled nano-cavities arising out of steric hindrance in both the biosensors are compared. Optimization of device dimensions has also been included in this work to enhance the sensitivity of the biosensors. It has been witnessed that the sensitivity of the proposed biosensor is ∼ 100% higher in Device structure-1 for neutral biomolecules with dielectric constant κ = 12, when compared to Device structure-2 for fully filled cavities. Whereas for the charged biomolecules, Device structure-1 shows ∼ 50% enhanced sensitivity than Device structure-2 for Nf= - 1 × 10 - 12C / cm 2. Device structure-1 demonstrates ∼ 120% higher sensitivity than Device structure-2 with partially filled cavities (i.e. 66% filled cavity). Finally, benchmarking of the proposed biosensor is presented with contemporary, state-of-the-art biosensors and it is highlighted that MoS2 FET-based biosensor emerges with a superior sensitivity of SVth = 0.81 V for κ= 12.

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Kumari, M., Singh, N. K., & Sahoo, M. (2022). A detailed investigation of dielectric-modulated dual-gate TMD FET based label-free biosensor via analytical modelling. Scientific Reports, 12(1). https://doi.org/10.1038/s41598-022-24677-6

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