Top-gated silicon nanowire transistors are fabricated by preparing all terminals (source, drain, and gate) on top of the nanowire in a single step via dose-modulated e-beam lithography. This outperforms other time-consuming approaches requiring alignment of multiple patterns, where alignment tolerances impose a limit on device scaling. We use as gate dielectric the 10-15 nm SiO(2) shell naturally formed during vapor-transport growth of Si nanowires, so the wires can be implemented into devices after synthesis without additional processing. This natural oxide shell has negligible leakage over the operating range. Our single-step patterning is a most practical route for realization of short-channel nanowire transistors and can be applied to a number of nanodevice geometries requiring nonequivalent electrodes.
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http://dx.doi.org/10.1021/nn900284b | DOI Listing |
Nat Commun
January 2025
School of Electronics Science and Engineering/National Laboratory of Solid-State Microstructures, Nanjing University, Nanjing, China.
Ultrathin silicon nanowires (diameter <30 nm) with strong electrostatic control are ideal quasi-1D channel materials for high-performance field effect transistors, while a short channel is desirable to enhance driving current. Typically, the patterning of such delicate channels relies on high-precision lithography, which is not applicable for large area electronics. In this work, we demonstrate that ultrathin and short silicon nanowires channels can be created through a local-curvature-modulated catalytic growth, where a planar silicon nanowires is directed to jump over a crossing step.
View Article and Find Full Text PDFACS Appl Electron Mater
January 2025
Electrical Engineering Division, Engineering Department, University of Cambridge, Cambridge CB3 0FA, U.K.
Nanoscale semiconductors offer significant advantages over their bulk semiconductor equivalents for electronic devices as a result of the ability to geometrically tune electronic properties, the absence of internal grain boundaries, and the very low absolute number of defects that are present in such small volumes of material. However, these advantages can only be realized if reliable contacts can be made to the nanoscale semiconductor using a scalable, low-cost process. Although there are many low-cost "bottom-up" techniques for directly growing nanomaterials, the fabrication of contacts at the nanoscale usually requires expensive and slow techniques like e-beam lithography that are also hard to scale to a level of throughput that is required for commercialization.
View Article and Find Full Text PDFNano Converg
January 2025
Bendable Electronics and Sustainable Technologies (BEST) Group, Electrical and Computer Engineering Department, Northeastern University, Boston, MA, 02115, USA.
The intriguing way the receptors in biological skin encode the tactile data has inspired the development of electronic skins (e-skin) with brain-inspired or neuromorphic computing. Starting with local (near sensor) data processing, there is an inherent mechanism in play that helps to scale down the data. This is particularly attractive when one considers the huge data produced by large number of sensors expected in a large area e-skin such as the whole-body skin of a robot.
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).
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