Quantifying signal changes in nano-wire based biosensors

Abstract

In this work, we present a computational methodology for predicting the change in signal (conductance sensitivity) of a nano-BioFET sensor (a sensor based on a biomolecule binding another biomolecule attached to a nano-wire field effect transistor) upon binding its target molecule. The methodology is a combination of the screening model of surface charge sensors in liquids developed by Brandbyge and co-workers [Sørensen et al., Appl. Phys. Lett., 2007, 91, 102105], with the PROPKA method for predicting the pH-dependent charge of proteins and protein-ligand complexes, developed by Jensen and co-workers [Li et al., Proteins: Struct., Funct., Bioinf., 2005, 61, 704-721, Bas et al., Proteins: Struct., Funct., Bioinf., 2008, 73, 765-783]. The predicted change in conductance sensitivity based on this methodology is compared to previously published data on nano-BioFET sensors obtained by other groups. In addition, the conductance sensitivity dependence from various parameters is explored for a standard wire, representative of a typical experimental setup. In general, the experimental data can be reproduced with sufficient accuracy to help interpret them. The method has the potential for even more quantitative predictions when key experimental parameters (such as the charge carrier density of the nano-wire or receptor density on the device surface) can be determined (and reported) more accurately.

TCAD-Based Simulation Method for the Electrolyte–Insulator–Semiconductor Field-Effect Transistor

“…2(a). We use the case of streptavidin, and its negative charge density is −2.78 × 10 13 cm −2 [11] in a PBS solution with a pH level of 7.4 [7]. We conduct the simulation by varying the distance (T tar ) between the charged sheet layer and the gate dielectric to examine the screening effect.…”

System-Level Sensitivity Analysis of SiNW-bioFET-Based Biosensing Using Lock-In Amplification

“…An extensive review of experimental research on these topics is available in [27]. De Vico et al were the first to investigate the most significant properties of the biofunctional layer in their computational model [13]. They succeeded in doing so by specifying a series of abstracted parameters helping to circumvent the intricacy of considering the complex laws and behaviours governing the interactions between the various biological elements of the system.…”

“…The SiNW-bioFET model on which we elaborate throughout the rest of this study is that of De Vico et al [23]. The device presents a circular cross-sectional channel as illustrated in figure 2.…”

“…p 0 corresponds to the dopant concentration N A and e is the elementary charge. The parameter Γ translates the sensitivity of the device according to equation 11, with I 0 , I 1 , K 0 , K 1 the modified Bessel functions of the first and second kind ; ε Si , ε ox , ε Sol stand for the relative permittivities of the SiNW, the aluminium gate oxide and the solution respectively [13], [28]. Finally, Γ l is a dimensionless parameter quantifying the effect of σ b and is defined by:…”

“…Such low LoDs can be reached by the fine tuning of static design parameters such as device dimensionality, and by carefully choosing the dynamic operation point at which measurements should be done. Several groups have already thoroughly documented the benefits of small cross-sectional SiNW channels or of biasing the sensor in the subthreshold regime [12], [13]. Although these investigations and experimental validation studies are sufficiently informative for many applications, they are limited in addressing the likely scenario of the integration of SiNWbioFETs in a custom-built diagnostics system such as the one we introduced in [14].…”