We report here the first realization of top-down silicon nanowires (SiNW) transduced by both junction-less field-effect transistor (FET) and the piezoresistive (PZR) effect. The suspended SiNWs are among the smallest top-down SiNWs reported to date, featuring widths down to ~20 nm. This has been achieved thanks to a 200 mm-wafer-scale, VLSI process fully amenable to monolithic CMOS co-integration. Thanks to the very small dimensions, the conductance of the silicon nanowire can be controlled by a nearby electrostatic gate. Both the junction-less FET and the previously demonstrated PZR transduction have been performed with the same SiNW. These self-transducing schemes have shown similar signal-to-background ratios, and the PZR transduction has exhibited a relatively higher output signal. Allan deviation (σA) of the same SiNW has been measured with both schemes, and we obtain σ(A) ~ 20 ppm for the FET detection and σ(A) ~ 3 ppm for the PZR detection at room temperature and low pressure. Orders of magnitude improvements are expected from tighter electrostatic control via changes in geometry and doping level, as well as from CMOS integration. The compact, simple topology of these elementary SiNW resonators opens up new paths towards ultra-dense arrays for gas and mass sensing, time keeping or logic switching systems on the SiNW-CMOS platform.
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http://dx.doi.org/10.1088/0957-4484/24/43/435203 | DOI Listing |
PLoS One
January 2025
College of Physics and Electronic Engineering, Hainan Normal University, HaiKou, China.
We have successfully prepared a significant number of nanowires from non-toxic silicon sources. Compared to the SiO silicon source used in most other articles, our preparation method is much safer. It provides a simple and harmless new preparation method for the preparation of silicon nanowires.
View Article and Find Full Text PDFACS Appl Bio Mater
January 2025
Graduate School of Science and Technology, Gunma University, 1-5-1 Tenjin-Cho, Kiryu, Gunma 376-8515, Japan.
Rapid and sensitive detection of virus-related antigens and antibodies is crucial for controlling sudden seasonal epidemics and monitoring neutralizing antibody levels after vaccination. However, conventional detection methods still face challenges related to compatibility with rapid, highly sensitive, and compact detection apparatus. In this work, we developed a Si nanowire (SiNW)-based field-effect biosensor by precisely controlling the process conditions to achieve the required electrical properties via complementary metal-oxide-semiconductor (CMOS)-compatible nanofabrication processes.
View Article and Find Full Text PDFSensors (Basel)
January 2025
Department of Computer Science, Faculty of Sciences and Humanities Sciences, Majmaah University, Al Majmaah 11952, Saudi Arabia.
Impedance-based biosensing has emerged as a critical technology for high-sensitivity biomolecular detection, yet traditional approaches often rely on bulky, costly impedance analyzers, limiting their portability and usability in point-of-care applications. Addressing these limitations, this paper proposes an advanced biosensing system integrating a Silicon Nanowire Field-Effect Transistor (SiNW-FET) biosensor with a high-gain amplification circuit and a 1D Convolutional Neural Network (CNN) implemented on FPGA hardware. This attempt combines SiNW-FET biosensing technology with FPGA-implemented deep learning noise reduction, creating a compact system capable of real-time viral detection with minimal computational latency.
View Article and Find Full Text PDFSensors (Basel)
December 2024
CNRS, LAAS, 7 Avenue du Colonel Roche, F-31400 Toulouse, France.
The development of ion-sensitive field-effect transistor (ISFET) sensors based on silicon nanowires (SiNW) has recently seen significant progress, due to their many advantages such as compact size, low cost, robustness and real-time portability. However, little work has been done to predict the performance of SiNW-ISFET sensors. The present study focuses on predicting the performance of the silicon nanowire (SiNW)-based ISFET sensor using four machine learning techniques, namely multilayer perceptron (MLP), nonlinear regression (NLR), support vector regression (SVR) and extra tree regression (ETR).
View Article and Find Full Text PDFChem Commun (Camb)
January 2025
Guangdong Provincial Key Laboratory of Nanophotonic Functional Materials and Devices, School of Information and Optoelectronic Science and Engineering, South China Normal University, Guangzhou 510006, China.
We fabricated flexible, three-dimensional (3D) ordered silicon nanowire (SiNW) arrays decorated with high-density silver nanoparticles (AgNPs) for the sensitive and reproducible detection of pesticide residues. These sensors demonstrated a detection limit of 10 M for methyl parathion (MPT) on curved surfaces.
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