Hassen Maaref

The kinetics of dewetting ultra-thin Si layers from silicon dioxide

In this study, we investigate the kinetically driven dewetting of ultra-thin silicon films on silicon oxide substrate under ultra-high vacuum, at temperatures where oxide desorption and silicon lost could be ruled out. We show that in ultra-clean experimental conditions, the three different regimes of dewetting, namely (i) nucleation of holes, (ii) film retraction and (iii) coalescence of holes, can be quantitatively measured as a function of temperature, time and thickness. For a nominal flat clean sample these three regimes co-exist during the film retraction until complete dewetting. To discriminate their roles in the kinetics of dewetting, we have compared the dewetting evolution of flat unpatterned crystalline silicon layers (homogeneous dewetting), patterned crystalline silicon layers (heterogeneous dewetting) and amorphous silicon layers (crystallization-induced dewetting). The first regime (nucleation) is described by a breaking time which follows an exponential evolution with temperature with an activation energy EH 3.2eV. The second regime (retraction) is controlled by surface diffusion of matter from the edges of the holes. It involves a very fast redistribution of matter onto the flat Si layer, which prevents the formation of a rim on the edges of the holes during both heterogeneous and homogeneous dewetting. The time evolution of the linear dewetting front measured during heterogeneous dewetting follows a characteristic power law x t 0.45 consistent with a surface diffusion-limited

Radiative N-localized recombination and confinement in GaAsN/GaAs epilayers and quantum well structures

Abstract The photoluminescence (PL) properties of GaAsN/GaAs epilayers and single quantum wells (QWs) have been investigated as a function of the excitation density and the sample temperature (10–300 K). At low temperatures, the PL spectra were sensitive to the excitation density for epilayers and QWs. For both structures, a blue shift of the PL peak is noted with increasing the excitation power. In contrast, the temperature dependence shows different behaviors for the bulk epilayers and for the quantum wells structures. An S-shape of the PL peak energy versus temperature has been observed for the GaAsN/GaAs epilayer while the QWs peak energy decreases monotically with the sample temperature and could be fitted by conventional Varshni’s law. This behavior is due to the exciton localization effect which is induced by the local fluctuation of nitrogen concentration.

Adaptive image compression technique for wireless sensor networks

When using wireless sensor networks for real-time image transmission, some critical points should be considered. These points are limited computational power, storage capability, narrow bandwidth and required energy. Therefore, efficient compression and transmission of images in wireless sensor network is considered. To address the above mentioned concerns, an efficient adaptive compression scheme that ensures a significant computational and energy reduction as well as communication with minimal degradation of the image quality is proposed. This scheme is based on wavelet image transform and distributed image compression by sharing the processing of tasks to extend the overall lifetime of the network. Simulation results are presented and they show that the proposed scheme optimizes the network lifetime, reduces significantly the amount of the required memory and minimizes the computation energy by reducing the number of arithmetic operations and memory accesses.

Adaptive image transfer for wireless sensor networks (WSNs)

When using wireless sensor networks for real-time data transmission, some critical points should be considered. Restricted computational power, memory limitations, narrow bandwidth and energy supplied present strong limits in sensor nodes. Therefore, maximizing network lifetime and minimizing energy consumption are always optimization goals. To reduce the energy consumption of the sensor network during image transmission, an energy efficient image compression scheme is proposed. The image compression scheme reduces the required memory. To address the abovementioned concerns, in this paper we describe an approach of image transmission in WSNs, taking advantage of JPEG2000 still image compression standard and using MATLAB and C from Jasper. JPEG2000 provides a practical set of features, not necessarily available in the previous standards. These features were achieved using techniques: the Discrete Wavelet Transform (DWT), and Embedded Block Coding with Optimized Truncation (EBCOT). Performance of the proposed image compression scheme is investigated with respect to image quality and energy consumption. Simulation results are presented and show that the proposed scheme optimizes network lifetime and reduces significantly the amount of required memory by analyzing the functional influence of each parameter of this distributed image compression algorithm.

Energy-efficient wavelet image compression in Wireless Sensor Network

In the recent years, the wireless technology would have known an exponential growth, which has an impact on developing and improving the field of telecommunications beyond the means of transmission wire to the radio frequency communication. The Wireless Sensor Network (WSN) is enrolled in this context. Its a collection of component (nodes) organized into a cooperative network. The main components of this network are tiny battery powered cameras with wireless communication capability. Therefore, image transfer in WSNs presents major challenge which raises issues related to its representation, its storage and its transmission. However, communication of image content has several bottlenecks, including limited bandwidth of cellular networks, restricted computational power, limited storage capability, and battery constraints of the appliances. In this paper, we address the energy, system lifetime and bandwidth bottlenecks of image communication. We present an efficient adaptive compression scheme that can significantly minimize the energy required for wireless image communication while meeting bandwidth constraints of wireless network and image quality. Based on Discrete Wavelet Transform, we propose an efficient image compression scheme, enabling significant reduction in computation energy needed with minimal degradation of image quality. Simulation results are done with C++ and show that the proposed scheme optimizes network lifetime, reduces significantly the amount of required memory and minimizes both (i) computation energy, by reducing the computation needed to compress an image and (ii) communication energy, consumed by the RF component which is proportional to the number of transmitted bits.

Investigation of the InAs/GaAs Quantum Dots’ Size: Dependence on the Strain Reducing Layer’s Position

This work reports on theoretical and experimental investigation of the impact of InAs quantum dots (QDs) position with respect to InGaAs strain reducing layer (SRL). The investigated samples are grown by molecular beam epitaxy and characterized by photoluminescence spectroscopy (PL). The QDs optical transition energies have been calculated by solving the three dimensional Schrödinger equation using the finite element methods and taking into account the strain induced by the lattice mismatch. We have considered a lens shaped InAs QDs in a pure GaAs matrix and either with InGaAs strain reducing cap layer or underlying layer. The correlation between numerical calculation and PL measurements allowed us to track the mean buried QDs size evolution with respect to the surrounding matrix composition. The simulations reveal that the buried QDs’ realistic size is less than that experimentally driven from atomic force microscopy observation. Furthermore, the average size is found to be slightly increased for InGaAs capped QDs and dramatically decreased for QDs with InGaAs under layer.

Anomaly and defects characterization by I-V and current deep level transient spectroscopy of Al0.25Ga0.75N/GaN/SiC high electron-mobility transistors

In this paper, we report static electric drain-source current-voltage measurements for different gate voltages and at different temperatures, performed on Al0.25Ga0.75N/GaN/SiC high electron-mobility transistors (HEMT). The results show the presence of kink and collapse effects. We have demonstrated that these effects are significant in the temperature range varying from 150 to 400 K with a maximum around 300 K. This parasitic effect was correlated with the presence of deep levels in our transistor. Indeed, we have noticed the presence of two electron traps named A1 and A2, and one hole trap named H1; their respective activation energies, which are determined using current deep level transient spectroscopy (CDLTS), are, respectively, 0.56, 0.82, and 0.75 eV. Traps H1 and A1 are shown to be extended defects in the Al0.25Ga0.75N/GaN heterostructure; they are supposed to be the origin of the kink and collapse effects. However, the punctual defect A2 seems to be located either in the free gate-drain surface, ...

Influence of Si(001) substrate misorientation on morphological and optical properties of Ge quantum dots

We have investigated the correlation between morphological and optical properties of Ge dots deposited by molecular-beam epitaxy on a Si(001) surface and on a high-index Si(118) vicinal surface. Ge islands were confined on the top of an undulated Si0.5Ge0.5 template layer according to the Stranski-Krastanov growth mode. Atomic force microscopy measurements reveal that the main effect of the vicinal substrate is to transform hut islands on a nominal (001) substrate into wire-shaped islands on (118) substrates. We have observed a direct correlation between the elongated shape and polarization anisotropy of optical transitions in island. The island photoluminescence (PL) emission is partially (∼25%) polarized for dots deposited on a (118) substrate. PL spectroscopy investigations as a function of temperature and excitation power are reported. The results show that the PL of islands strongly depends on the pump excitation power: it broadens and is blueshifted by 28 and 14meV∕decade for structures grown on (00...

Energy conservation for image transmission over wireless sensor networks

Images processing is one of the most important applications for wireless sensor networks. One of the major challenges in enabling images transmission in wireless sensor network will be the need to process and wirelessly transmit very large amounts of data. Therefore, optimized computation and energy dissipation are critical requirements to maximize the lifetime of the sensor network. To address this problem, the energy efficient image compression over a resource constrained multi-hop wireless network is considered. Adaptive Wavelet Image Compression and Organization (AWICOA) is an algorithm that ensures efficient energy dissipation and low computation overhead on the sensor nodes. The proposed algorithm is investigated with respect to energy consumption and image quality. Performance studies indicate that the proposed algorithm enabling significant reductions in computation as well as communication energy needed.

Improved feature extraction method based on Histogram of Oriented Gradients for pedestrian detection

In recent years, pedestrian detection for Automobile Driver Assistance System (ADAS) is a primordial task in the smart vehicle. Histogram of oriented gradients (HoG) is one of the most effective pedestrian feature extraction approaches to the study. In this paper, an optimization of pedestrian detection based on HOG method is presented and investigated to achieve an accurate human detection system. The study of different computation steps of the standard algorithm shows the possibility of improving the system performance, specifically in the build histograms step. The main idea is to customize each bin weight according to its contribution in the pedestrian extracted features. Actually, the different bins of a HoG improved vector that encodes a single cell will not have the same weight. Indeed, after the histograms computation, we will distribute an amplification factor for each bin in order to increase the weight bins that describe the relevant pedestrian features from a side. Top of that, we were interested to decrease the bins weight that affect the irrelevant features such as, other obstacle or the image background. The classification system is performed using a linear SVM classifier which is simple and easy to implement in ADAS applications. The performance studies using MATLAB simulation, proves the effectiveness of our approach.