Nanometer Scale Electronic Device Integration Using Side-Wall Deposition and Etch-Back Technology doi: https://doi.org/10.13052/jmmc2246-137X.131
U. Hilleringmann1, F. Assion1, F. F. Vidor2 and G. I.Wirth2
1University of Paderborn, Fac. of Computer Science, Electrical Engineering
and Mathematics, Sensor Technology Group, 33095 Paderborn, Germany,
2Universidade Federal do Rio Grande do Sul Escola de Engenharia,
Departamento de Engenharia Elétrica, Av. Osvaldo Aranha, 103,
Porto Alegre, RS – Brasil,
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Abstract: Side-wall deposition and etch-back technology is a cheap method to produce
nanometer scale lines and trenches or gaps without expensive equipment like
high resolution lithography or chemical-mechanical polishing. It can be used
for gratings in integrated optics and in semiconductor technology for electronic
device integration. This paper reflects its application for field effect transistors
in bulk silicon and demonstrates its potential for nanometer scale particle
transistor integration. Silicon and ZnO nanoparticle field effect transistors
using different setup structures integrated by side-wall deposition and etchback
show on/off ratios of up to 4500 and mobilities up to some cm2 V−1s −1.
Although the best structures apply high temperature processing, a reduced
temperature process for ZnO nanoparticle transistor integration on glass and
foil substrates is presented.
Keywords: nano particle transistor, deposition defined structure; nanoscale
trench; ZnO.
Abstract: This paper discusses the architectures, interfaces as well as behaviours of
our model of an implemented intelligent device, a Robot. As a case study,
the Robot has all the key features of the Internet of Intelligent Things
(IoT): autonomous, mobile, and sensorial. By achieving the goal of having
a computer or smart phone to access and control the Robot, we present an
approach to control and monitor a remote object to move through the Internet.
Keywords: Internet of Things; Embedded System; Sensor; Measurement
and Control Technology.
Abstract: This paper presents a differential low noise amplifier (LNA) based on
a new configuration suitable for low-power and low noise applications.
By inserting additional positive feedback capacitor connected to drain
and source terminal of the cascode transistor, this proposed configuration
increases voltage gain because of decreasing the total transconductance
by a factor generated a negative conductance. In addition, the differential
structure and power-constrained simultaneous noise and input matching
(PCSNIM) technique are chosen simultaneously to perform the input matching
and to improve the noise figure at the desired band. Using TSMC
0.18μmRF CMOS process, the proposed LNA exhibit a state of the art
performance consuming only 7.58mW from a 1.8V power supply. Input
and output return loss of the LNA are below than −13dB while achieves
a power gain of 18.66 dB and a noise figure of 2.03 dB at the band of
interest.
Abstract: One of the most important IoT challenges is scalability. This paper addresses
this issue by introducing pattern recognition services into the lower layers of
the IoT reference model stack and reduces the processing at the higher layers.
The work adopts the reference model developed by the IoT-A project and
the LinkSmart Middleware platform. The new architecture implementation
extends the LinkSmart introducing a pattern recognition manager that includes
algorithms to estimate parameters, detect outliers, and to perform clustering
of raw data from IoT resources. The new module is integrated with the Big
Data Haddop platform and uses Mahout algorithms implementation.
Keywords: Internet of Things, Big Data, Architecture.
Abstract: Assurance network technologies are necessary to produce trustworthy terminals
or infrastructure service applications for potentially large-scale networks
such as in the Internet-of-Things. In large-scale network systems, not only the
size of the network will become larger but also the density will vary in the
network. Mobile ad hoc networks (MANETs) could be the future of networks.
In the future, the size of networks will become larger. In real situations, nodes
are not uniformly distributed in networks. The difference in node density will
be greater in large-scale networks. When flooding is conducted in a dense area,
network congestion occurs and nodes cannot communicate with each other.
As proposed in paper [1], Route-Split Routing (RSR) can communicate
with a higher throughput than theAdhoc On-demand DistanceVector(AODV)
method in largeMANETs.Along with the assurance network design principle,
RSR splits its route by configuring Sub-route Management Nodes (SMNs)
using a constant value called the SMN interval. In large MANETs, nodes
are not distributed evenly. Thus, routing protocols for large MANETs must
take into consideration that its route will pass through sparse areas and dense
areas. In this paper, we propose a new routing method based on RSR for large MANETs with uneven node distribution by using the assurance network design
principle. Our proposed method aims to avoid unnecessary rebroadcasts,
contentions, and collisions caused by flooding in dense areas. To avoid the
broadcast storm problem, we propose a new route repair method with RSR.
Our proposed method can communicate with much higher throughput than
RSR and AODV in a large MANET with non-uniform density. We have
implemented our proposed method on a simulator and evaluated it through
simulation experiments. We have observed that our proposed method is
effective in large MANETs with uneven node distribution. The effectiveness
of our proposed method varies with a configurable parameter called the SMN
interval. Our proposed method with an SMN interval of 2 is the most effective
among the tested SMN intervals 2, 5, and 8.
Keywords: Mobile ad hoc networks, Assurance networks, Opportunistic
broadcast, Broadcast storm, Route-split routing.