A Calibration Method for Nanowire Biosensors to Suppress Device-to-Device Variation

Abstract

Nanowire/nanotube biosensors have stimulated significant interest; however the inevitable device-to-device variation in the biosensor performance remains a great challenge. We have developed an analytical method to calibrate nanowire biosensor responses that can suppress the device-to-device variation in sensing response significantly. The method is based on our discovery of a strong correlation between the biosensor gate dependence (dIds/dVg) and the absolute response (absolute change in current, ΔI). In2O3 nanowire based biosensors for streptavidin detection were used as the model system. Studying the liquid gate effect and ionic concentration dependence of strepavidin sensing indicates that electrostatic interaction is the dominant mechanism for sensing response. Based on this sensing mechanism and transistor physics, a linear correlation between the absolute sensor response (ΔI) and the gate dependence (dIds/dVg) is predicted and confirmed experimentally. Using this correlation, a calibration method was developed where the absolute response is divided by dIds/dVg for each device, and the calibrated responses from different devices behaved almost identically. Compared to the common normalization method (normalization of the conductance/resistance/current by the initial value), this calibration method was proved advantageous using a conventional transistor model. The method presented here substantially suppresses device-to-device variation, allowing the use of nanosensors in large arrays.

Supersensitive fingerprinting of explosives by chemically modified nanosensors arrays

“…1A,B). The resulting 144 nanosensor devices were electrically characterized and effectively normalized by their respective transconductance values, thus minimizing the device-to-device signal variability 30 , Fig. 2.…”

“…Clearly, the calibrated responses of the devices to the analyte (DI sd ), when normalized by their respective transconductance values (device transconductance ðG m ¼ dIds dVg Þ), Fig. 2b, will result in relatively small device-to-device variability, even for originally largely dissimilar devices 30 , Fig. 2c.…”

Field-effect sensors – from pH sensing to biosensing: sensitivity enhancement using streptavidin–biotin as a model system

“…119 The reason for this reduction in device-to-device ratio is that the absolute current is directly dependent upon geometric parameters and threshold voltage of the device, whereas the normalised change in current is only subject to device-to-device variation from differences in the device threshold voltage. 119 Further, the change in current (ΔI) can either be positive or negative and consequently the normalised change in current, I norm , also has an associated sign. Some authors have reported the normalised change in current without this sign which makes the polarity of the measured change ambiguous.…”

“…In the subthreshold region, the transconductance is variable as a function of gate voltage and therefore cannot be treated as a constant. For FET-sensors operated in the linear regime, ΔV T can be extracted experimentally by simply dividing the current-response data (ΔI) by the device transconductance, g m : 29,61,119,128 …”

“…Assumption (a) has been widely experimentally evidenced for biosensing and pH sensing. 30,35,70,119,180 Assumption (b) has been shown to be approximately the case for pH sensing and assuming a consistent oxide material between devices. 181,182 As a result of this, pH sensing FET Sensitivity can be predicted via eqn (9) prior to pH sensing experiments simply by measuring the subthreshold slope and using knowledge of the oxide material.…”