As a prerequisite to the development of real label-free bioassay applications, a high-throughput top–down nanofabrication process is carried out with a combination of nanoimprint lithography, anisotropic wet-etching, and photolithography methods realizing nanoISFET arrays that are then analyzed for identical sensor characteristics. Here, a newly designed array-based sensor chip exhibits 32 high aspect ratio silicon nanowires (SiNWs) laid out in parallel with 8 unit groups that are connected to a very highly doped, Π-shaped common source and individual drain contacts. Intricately designed contact lines exert equal feed-line resistances and capacitances to homogenize the sensor response as well as to minimize parasitic transport effects and to render easy integration of a fluidic layer on top. The scalable nanofabrication process as outlined in this article casts out a total of 2496 nanowires (NWs) on a 4 inch p-type silicon-on-insulator (SOI) wafer, yielding 78 sensor chips based on nanoISFET arrays. The sensor platform exhibiting high-performance transistor characteristics in buffer solutions is thoroughly characterized using state-of-the-art surface and electrical measurement techniques. Deploying a pH sensor in liquid buffers after high-quality gas-phase silanization, nanoISEFT arrays demonstrate typical pH sensor behavior with sensitivity as high as 43 ± 3 mV·pH–1 and a device-to-device variation of 7% at the wafer scale. Demonstration of a high-density sensor platform with uniform characteristics such as nanoISFET arrays of silicon (Si) in a routine and refined nanofabrication process may serve as an ideal solution deployable for real assay-based applications.

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Silicon nanowires (SiNW) are highly sensitive to biomolecules. In some publications, changes of SiNW conductance in relation to their concentration levels are displayed. Upon binding, biomolecule charges change the surface potential and, thereby, the SiNW conductance. We discussed earlier that SiNWs can be regarded as long-channel, ion-sensitive field-effect transistors (ISFETs). The choice of a stable working point is important and defines the SiNW conductance. The common detection principle is based on the shift in threshold voltage. Regardless of conductance change or threshold voltage shift, relative values are related to biomolecule concentrations. However, potentiometric detection suffers from Debye screening of biomolecule charges by counter ions of the test solution. This makes biosensing in physiological buffer solutions difficult if not impossible. In this report, a method for impedance sensing with SiNWs, which was earlier used for ISFET devices is introduced. This method gains comparable results to potentiometric sensing. The change of interface impedance is indirectly linked with the biomolecule charges. In addition, the dielectric property of the interface layer plays an important role. At elevated frequencies, our method can be regarded as an alternative mechanism similar to dielectric spectroscopy at low frequencies. Thereby, Debye screening does no longer dominate the recordings.Nowadays, the detection of DNA is essential and indispensable in biomedicine. There are many scientific applications, in which the identification of the base pair sequence is crucial. Applications can be found in criminology, i.e., forensics, [1][2][3] determination of gene mutations in plants, [4][5][6] DNA detection in biotechnology, [7][8][9] and in the detection of different, genetically predetermined diseases in medicine. [10][11][12] Within all these applications, the possibility of a fully electronic, labelfree DNA detection is getting more and more interesting to provide a quick and reliable readout. This is especially needed in forensics to convict a perpetrator and in medicine for the fast diagnosis of diseases so that the patient can immediately be medicated or operated. Moreover, a swift DNA detection is desired for biological warfare agents. [13] Since the first publication of Piet Bergveld and coworkers in 1970, [14] ion-sensitive field-effect transistors (ISFETs) are applied as biosensors for biomolecules, such as DNA, [15][16][17][18][19] enzymes, [20][21][22] and proteins. [23][24][25] Furthermore, it was shown that it is also possible to record cellular signals with ISFETs. [26][27][28] In the past decades, the application of silicon nanowires (SiNWs) attracted more and more attention in terms of extracellular recordings [29][30][31] and biomolecule sensing, like the detection of DNA immobilization, [32] hybridization, [33] and single nucleotide polymorphism [34][35][36] as well as antibody-antigen interactions. [37,38] In an earlier publication, we discussed that our silicon nanowire devices can be rega...

In this work, we demonstrate a behavioral PSPICE model for silicon nanowire (SiNW) field-effect transistor (FET) biosensors, which is suitable to simulate frequency domain electrical measurements. The model is divided into two separated components: an electrochemical part for the liquid/solid interface at the gate input and an FET part simulating the SiNW characteristics. In our study, the parameters of the FET model are obtained from characterization measurements of real devices, which are fabricated in our research group. Measurements were performed with a self-developed readout system. The model can be used to investigate the effects of solution conductivity and the drain and source capacitances of the SiNW sensor. We observed that the impedance spectra consist of a low-pass filter domain at low frequencies and a resonance domain at higher frequencies. The former is mainly influenced by the source capacitance and the oxide/liquid interface of the devices, whereas the latter is mainly dominated by the drain capacitance, in case that the solution conductivity is kept constant. In addition, based on the relationship of the solution conductivity and the experimental data, we suggest a value called representative resistance of the solution. In this study, the model was compared to real spectra and we achieved good agreement between simulation and experiments. Our model provides a clearer view for frequency domain measurements with SiNW sensors, offers the possibility to optimize our SiNW design and forms the basis to include and explain sensing events in future biomedical assays.

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