Silicon oxide (SiO2) is a good dielectric material in metal-oxide-semiconductor (MOS) structures. The improved SiO2 quality requires adequate study of doping diffusion in this structure to maintain the absence of the different impurities in the interface Poylsilicon/SiO2. For this we studied a theoretical model of boron diffusion before and after thermal annealing in a highly-doped polysilicon films. The model takes into account the distribution of vacancy mechanism by associating parameters and effects related to high concentrations. Based on the literature the model is solved using the engineering software tool MATLAB, following a well-defined algorithm. The model is validated with the help of simulation results obtained from Silvaco.

Silicon oxide (SiO2) is a good dielectric material in metal-oxide-semiconductor (MOS) structures. The improved SiO2 quality requires adequate study of doping diffusion in this structure to maintain the absence of the different impurities in the interface Poylsilicon/SiO2. For this we studied a theoretical model of boron diffusion before and after thermal annealing in a highly-doped polysilicon films. The model takes into account the distribution of vacancy mechanism by associating parameters and effects related to high concentrations. Based on the literature the model is solved using the engineering software tool MATLAB, following a well-defined algorithm. The model is validated with the help of simulation results obtained from Silvaco.

Silicon oxide (SiO2) is a good dielectric material in metal-oxide-semiconductor (MOS) structures. The improved SiO2 quality requires adequate study of doping diffusion in this structure to maintain the absence of the different impurities in the interface Poylsilicon/SiO2. For this we studied a theoretical model of boron diffusion before and after thermal annealing in a highly-doped polysilicon films. The model takes into account the distribution of vacancy mechanism by associating parameters and effects related to high concentrations. Based on the literature the model is solved using the engineering software tool MATLAB, following a well-defined algorithm. The model is validated with the help of simulation results obtained from Silvaco.

An 8-bit 40-MS/s low power Multiplying Digital-toAnalog Converter (MDAC) for a pipelined-to-Analog to Digital converter (ADC) is presented.The conventional dedicated operational amplifier (Op-Amp) isperformed by using telescopic architecture that features low power and less-area. Further reduction of power and area is achieved by using multifunction 1.5bit/stage MDAC architecture. The design of the Op-Amp is performed by the elaboration of a program based on multiobjective genetic algorithms to allow automated optimization. The proposed program is used tofind the optimal transistors sizes (length and width) in order to obtain the best Op-Amp performances for the MDAC. In this study, six performances are considered, direct current gain, unity-gain bandwidth, phase margin, power consumption, area, slew rate, thermal noise, and signal to noise ratio. The Matlab optimization toolbox is used to implement the program. Simulations were performed by using Cadence Virtuoso Spectre circuit simulator in standard AMS 0.18μm CMOS technology. A goodagreement is observed between the results obtained bythe program optimization and simulation, after that the Op-Ampis introduced in the MDAC circuit to extract its performances.

An 8-bit 40-MS/s low power Multiplying Digital-toAnalog Converter (MDAC) for a pipelined-to-Analog to Digital converter (ADC) is presented.The conventional dedicated operational amplifier (Op-Amp) isperformed by using telescopic architecture that features low power and less-area. Further reduction of power and area is achieved by using multifunction 1.5bit/stage MDAC architecture. The design of the Op-Amp is performed by the elaboration of a program based on multiobjective genetic algorithms to allow automated optimization. The proposed program is used tofind the optimal transistors sizes (length and width) in order to obtain the best Op-Amp performances for the MDAC. In this study, six performances are considered, direct current gain, unity-gain bandwidth, phase margin, power consumption, area, slew rate, thermal noise, and signal to noise ratio. The Matlab optimization toolbox is used to implement the program. Simulations were performed by using Cadence Virtuoso Spectre circuit simulator in standard AMS 0.18μm CMOS technology. A goodagreement is observed between the results obtained bythe program optimization and simulation, after that the Op-Ampis introduced in the MDAC circuit to extract its performances.

Data aggregation has emerged as a basic concept in wireless sensor networks. By combining data and eliminating redundancy, this concept showed its effectiveness in terms of reducing resources consumption. The data aggregation problem is not new and has been widely studied in wireless sensor networks. However, most of the proposed solutions are based on a static data aggregation scheme and do not consider the network constraints evolution such as energy bandwidth, overhead and transmission delay. To dynamically optimize the tradeoff between the data aggregation process and the network constraints, a new paradigm has been introduced recently named the feedback control system. The main idea is to adapt the data aggregation degree to the environment changes and the sensor network applications. The feedback control for data aggregation remains relatively a new research area and only few works have introduced this concept. Therefore this work points out this research field by surveying the different existing protocols.

Data aggregation has emerged as a basic concept in wireless sensor networks. By combining data and eliminating redundancy, this concept showed its effectiveness in terms of reducing resources consumption. The data aggregation problem is not new and has been widely studied in wireless sensor networks. However, most of the proposed solutions are based on a static data aggregation scheme and do not consider the network constraints evolution such as energy bandwidth, overhead and transmission delay. To dynamically optimize the tradeoff between the data aggregation process and the network constraints, a new paradigm has been introduced recently named the feedback control system. The main idea is to adapt the data aggregation degree to the environment changes and the sensor network applications. The feedback control for data aggregation remains relatively a new research area and only few works have introduced this concept. Therefore this work points out this research field by surveying the different existing protocols.

Data aggregation has emerged as a basic concept in wireless sensor networks. By combining data and eliminating redundancy, this concept showed its effectiveness in terms of reducing resources consumption. The data aggregation problem is not new and has been widely studied in wireless sensor networks. However, most of the proposed solutions are based on a static data aggregation scheme and do not consider the network constraints evolution such as energy bandwidth, overhead and transmission delay. To dynamically optimize the tradeoff between the data aggregation process and the network constraints, a new paradigm has been introduced recently named the feedback control system. The main idea is to adapt the data aggregation degree to the environment changes and the sensor network applications. The feedback control for data aggregation remains relatively a new research area and only few works have introduced this concept. Therefore this work points out this research field by surveying the different existing protocols.

Data aggregation has emerged as a basic concept in wireless sensor networks. By combining data and eliminating redundancy, this concept showed its effectiveness in terms of reducing resources consumption. The data aggregation problem is not new and has been widely studied in wireless sensor networks. However, most of the proposed solutions are based on a static data aggregation scheme and do not consider the network constraints evolution such as energy bandwidth, overhead and transmission delay. To dynamically optimize the tradeoff between the data aggregation process and the network constraints, a new paradigm has been introduced recently named the feedback control system. The main idea is to adapt the data aggregation degree to the environment changes and the sensor network applications. The feedback control for data aggregation remains relatively a new research area and only few works have introduced this concept. Therefore this work points out this research field by surveying the different existing protocols.