A compact model able to predict the electrical read-out of field-effect biosensors based on two-dimensional (2D) semiconductors is introduced. It comprises the analytical description of the electrostatics including the charge density in the 2D semiconductor, the site-binding modeling of the barrier oxide surface charge, and the Stern layer plus an ion-permeable membrane, all coupled with the carrier transport inside the biosensor and solved by making use of the Donnan potential inside the ion-permeable membrane formed by charged macromolecules. This electrostatics and transport description account for the main surface-related physical and chemical processes that impact the biosensor electrical performance, including the transport along the low-dimensional channel in the diffusive regime, electrolyte screening, and the impact of biological charges. The model is implemented in Verilog-A and can be employed on standard circuit design tools. The theoretical predictions obtained with the model are validated against measurements of a MoS2 field-effect biosensor for streptavidin detection showing excellent agreement in all operation regimes and leading the way for the circuit-level simulation of biosensors based on 2D semiconductors.

Two-dimensional material (2DM)-based Field-Effect Transistors (FETs) have been postulated as a solid alternative for biosensing applications thanks to: (i) the possibility to enable chemical sensitivity by functionalization, (ii) an atomically thin active area which guarantees optimal electrostatic coupling between the sensing layer and the electronic active region, and (iii) their compatibility with large scale fabrication techniques. Although 2DM-based BioFETs have demonstrated notable sensing capabilities, other relevant aspects, such as the yield or device-to-device variability, will demand further evaluation in order to move them from lab-to-fab applications. Here, we focus on the latter aspect by analyzing the performance of MoS2-based BioFETs for the detection of DNA molecules. In particular, we explore the impact of the randomized location and activation of the receptor molecules at the sensing interface on the device response. Several sensing interface configurations are implemented, so as to evaluate the sensitivity dependence on device-to-device variability.

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