In order to design and optimize high-sensitivity silicon nanowire-field-effect transistor (SiNW FET) pressure sensors, this paper investigates the effects of channel orientations and the uniaxial stress on the ballistic hole transport properties of a strongly quantized SiNW FET placed near the high stress regions of the pressure sensors. A discrete stress-dependent six-band k.p method is used for subband structure calculation, coupled to a two-dimensional Poisson solver for electrostatics. A semi-classical ballistic FET model is then used to evaluate the ballistic current-voltage characteristics of SiNW FETs with and without strain. Our results presented here indicate that [110] is the optimum orientation for the p-type SiNW FETs and sensors. For the ultra-scaled 2.2 nm square SiNW, due to the limit of strong quantum confinement, the effect of the uniaxial stress on the magnitude of ballistic drive current is too small to be considered, except for the [100] orientation. However, for larger 5 nm square SiNW transistors with various transport orientations, the uniaxial tensile stress obviously alters the ballistic performance, while the uniaxial compressive stress slightly changes the ballistic hole current. Furthermore, the competition of injection velocity and carrier density related to the effective hole masses is found to play a critical role in determining the performance of the nanotransistors.
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http://dx.doi.org/10.3390/s90402746 | DOI Listing |
Sensors (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 PDFBiosens Bioelectron
January 2025
Department of Chemical and Materials Engineering, National Central University, Jhong-Li, 32001, Taiwan. Electronic address:
Ultra-low concentration nucleic acid detection is crucial for disease diagnosis and prognosis. Silicon nanowire field-effect transistors (SiNW FETs) are promising due to their sensitivity, real-time capabilities, and compact design. A critical consideration for FETs is the reaction time required for nucleic acid diffusion to the detection surface, especially at low concentrations.
View Article and Find Full Text PDFACS Sens
November 2024
School of Electronic Science and Engineering/National Laboratory of Solid-State Microstructures, Nanjing University, 210023 Nanjing, China.
ACS Nano
August 2024
State Key Laboratory of Transducer Technology, Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, Shanghai 200050, China.
The silicon nanowire field-effect transistor (SiNW FET) has been developed for over two decades as an ultrasensitive, label-free biosensor for biodetection. However, inconsistencies in manufacturing and surface functionalization at the nanoscale have led to poor sensor-to-sensor consistency in performance. Despite extensive efforts to address this issue through process improvements and calibration methods, the outcomes have not been satisfactory.
View Article and Find Full Text PDFTalanta
October 2024
Institute of Microelectronics of the Chinese Academy of Sciences, Beijing 100029, China; University of Chinese Academy of Sciences, Beijing 101408, China.
Exosomes are of great significance in clinical diagnosis, due to their high homology with parental generation, which can reflect the pathophysiological status. However, the quantitative and classification detection of exosomes is still faced with the challenges of low sensitivity and complex operation. In this study, we develop an electrical and label-free method to directly detect exosomes with high sensitivity based on a Silicon nanowire field effect transistor biosensor (Si-NW Bio-FET).
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