Understanding and Mapping Sensitivity in MoS2 Field-Effect-Transistor-Based Sensors.
Sensors based on two-dimensional (2D) field-effect transistors (FETs) are extremely sensitive and can detect charged analytes with attomolar limits of detection (LOD). Despite some impressive LODs, the operating mechanisms and factors that determine the signal-to-noise ratio in 2D FET-based sensors remain poorly understood. These uncertainties, coupled with an expansive design space for sensor layout and analyte positioning, result in a field with many reported highlights but limited collective progress. Here, we provide insight into sensing mechanisms of 2D molybdenum disulfide (MoS2) FETs by realizing precise control over the position and charge of an analyte using a customized atomic force microscope (AFM), with the AFM tip acting as an analyte. The sensitivity of the MoS2 FET channel is revealed to be nonuniform, manifesting sensitive hotspots with locations that are stable over time. When the charge of the analyte is varied, an asymmetry is observed in the device drain-current response, with analytes acting to turn the device off leading to a 2.5× increase in the signal-to-noise ratio (SNR). We developed a numerical model, applicable to all FET-based charge-detection sensors, that confirms our experimental observation and suggests an underlying mechanism. Further, extensive characterization of a set of different MoS2 FETs under various analyte conditions, coupled with the numerical model, led to the identification of three distinct SNRs that peak with dependence on the layout and operating conditions used for a sensor. These findings reveal the important role of analyte position and coverage in determining the optimal operating bias conditions for maximal sensitivity in 2D FET-based sensors, which provides key insights for future sensor design and control.
Duke Scholars
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- Nanoscience & Nanotechnology
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Published In
DOI
EISSN
ISSN
Publication Date
Volume
Issue
Start / End Page
Related Subject Headings
- Nanoscience & Nanotechnology