We herein report the design of a novel semiconducting silicon nanowire field-effect transistor (SiNW-FET) biosensor array for ultrasensitive label-free and real-time detection of nucleic acids. Highly responsive SiNWs with narrow sizes and high surface-to-volume-ratios were "top-down" fabricated with a complementary metal oxide semiconductor compatible anisotropic self-stop etching technique. When SiNWs were covalently modified with DNA probes, the nanosensor showed highly sensitive concentration-dependent conductance change in response to specific target DNA sequences. This SiNW-FET nanosensor revealed ultrahigh sensitivity for rapid and reliable detection of 1 fM of target DNA and high specificity single-nucleotide polymorphism discrimination. As a proof-of-concept for multiplex detection with this small-size and mass producible sensor array, we demonstrated simultaneous selective detection of two pathogenic strain virus DNA sequences (H1N1 and H5N1) of avian influenza.

Silicon nanowire (SiNW) field effect transistors (FETs) have emerged as powerful sensors for ultrasensitive, direct electrical readout, and label-free biological/chemical detection. The sensing mechanism of SiNW-FET can be understood in terms of the change in charge density at the SiNW surface after hybridization. So far, there have been limited systematic studies on fundamental factors related to device sensitivity to further make clear the overall effect on sensing sensitivity. Here, we present an analytical result for our triangle cross-section wire for predicting the sensitivity of nanowire surface-charge sensors. It was confirmed through sensing experiments that the back-gated SiNW-FET sensor had the highest percentage current response in the subthreshold regime and the sensor performance could be optimized in low buffer ionic strength and at moderate probe concentration. The optimized SiNW-FET nanosensor revealed ultrahigh sensitivity for rapid and reliable detection of target DNA with a detection limit of 0.1 fM and high specificity for single-nucleotide polymorphism discrimination. In our work, enhanced sensing of biological species by optimization of operating parameters and fundamental understanding for SiNW FET detection limit was obtained.

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