Ahcène Bounceur

Evaluation of Analog/RF Test Measurements at the Design Stage

We present a method that is capable of handling process variations to evaluate analog/RF test measurements at the design stage. The method can readily be used to estimate test metrics, such as parametric test escape and yield loss, with parts per million accuracy, and to fix test limits that satisfy specific tradeoffs between test metrics of interest. Furthermore, it provides a general framework to compare alternative test solutions that are continuously being proposed toward reducing the high cost of specification-based tests. The key idea of the method is to build a statistical model of the circuit under test and the test measurements using nonparametric density estimation. Thereafter, the statistical model can be simulated very fast to generate an arbitrarily large volume of new data. The method is demonstrated for a previously proposed built-in self-test measurement for low-noise amplifiers. The result indicates that the new synthetic data have the exact same structure of data generated by a computationally intensive brute-force Monte Carlo circuit simulation.

Estimation of Test Metrics for the Optimisation of Analogue Circuit Testing

The estimation of test metrics such as defect level, test yield or yield loss is important in order to quantify the quality and cost of a test approach. For design-for-test purposes, this is important in order to select the best test measurements but this must be done at the design stage, before production test data is made available. In the analogue domain, previous works have considered the estimation of these metrics for the case of single faults, either catastrophic or parametric. The consideration of single parametric faults is sensitive for a production test technique if the design is robust. However, in the case that production test limits are tight, test escapes resulting from multiple parametric deviations may become important. In addition, aging mechanisms result in field failures that are often caused by multiple parametric deviations. In this paper, we will consider the estimation of analogue test metrics under the presence of multiple parametric deviations (or process deviations) and under the presence of faults. A statistical model of a circuit is used for setting test limits under process deviations as a trade-off between test metrics calculated at the design stage. This model is obtained from a Monte Carlo circuit simulation, assuming Gaussian probability density functions (PDFs) for the parameter and performance deviations. After setting the test limits considering process deviations, the test metrics are calculated under the presence of catastrophic and parametric single faults for different potential test measurements. We will illustrate the technique for the case of a fully differential operational amplifier, proving the validity in the case of this circuit of the Gaussian PDF.

Dynamic clustering and management of mobile wireless sensor networks

In Wireless Sensor Networks (WSNs), routing data towards the sink leads to unbalanced energy consumption among intermediate nodes resulting in high data loss rate. The use of multiple Mobile Data Collectors (MDCs) has been proposed in the literature to mitigate such problems. MDCs help to achieve uniform energy-consumption across the network, fill coverage gaps, and reduce end-to-end communication delays, amongst others. However, mechanisms to support MDCs such as location advertisement and route maintenance introduce significant overhead in terms of energy consumption and packet delays. In this paper, we propose a self-organizing and adaptive Dynamic Clustering (DCMDC) solution to maintain MDC-relay networks. This solution is based on dividing the network into well-delimited clusters called Service Zones (SZs). Localizing mobility management traffic to a SZ reduces signaling overhead, route setup delay and bandwidth utilization. Network clustering also helps to achieve scalability and load balancing. Smaller network clusters make buffer overflows and energy depletion less of a problem. These performance gains are expected to support achieving higher information completeness and availability as well as maximizing the network lifetime. Moreover, maintaining continuous connectivity between the MDC and sensor nodes increases information availability and validity. Performance experiments show that DCMDC outperforms its rival in the literature. Besides the improved quality of information, the proposed approach improves the packet delivery ratio by up to 10%, end-to-end delay by up to 15%, energy consumption by up to 53%, energy balancing by up to 51%, and prolongs the network lifetime by up to53%.

Estimation of Analog Parametric Test Metrics Using Copulas

A new technique for the estimation of analog parametric test metrics at the design stage is presented in this paper. This technique employs the copulas theory to estimate the distribution between random variables that represent the performances and the test measurements of the circuit under test (CUT). A copulas-based model separates the dependencies between these random variables from their marginal distributions, providing a complete and scale-free description of dependence that is more suitable to be modeled using well-known multivariate parametric laws. The model can be readily used for the generation of an arbitrarily large sample of CUT instances. This sample is thereafter used for estimating parametric test metrics such as defect level (or test escapes) and yield loss. We demonstrate the usefulness of the proposed technique to evaluate a built-in-test technique for a radio frequency low noise amplifier and to set test limits that result in a desired tradeoff between test metrics. In addition, we compare the proposed technique with previous ones that rely on direct density estimation.

Pseudorandom Functional BIST for Linear and Nonlinear MEMS

Pseudorandom test techniques are widely used for measuring the impulse response (IR) for linear devices and Volterra kernels for nonlinear devices, especially in the acoustics domain. This paper studies the application of pseudorandom functional test techniques to linear and nonlinear MEMS built-in-self-test (BIST). We will first present the classical pseudorandom BIST technique for linear time invariant (LTI) systems which is based on the evaluation of the IR of the device under test (DUT) stimulated by a maximal length sequence (MLS). Then we will introduce a new type of pseudorandom stimuli called the inverse-repeat sequence (IRS) that proves better immunity to noise and distortion than MLS. Next, we will illustrate the application of these techniques for weakly nonlinear, purely nonlinear and strongly nonlinear devices

A Wireless Sensor Network Border Monitoring System: Deployment Issues and Routing Protocols

External border surveillance is critical to the security of every state and the challenges it poses are changing and likely to intensify. Wireless sensor networks (WSN) are a low cost technology that provide an intelligence-led solution to effective continuous monitoring of large, busy, and complex landscapes. The linear network topology resulting from the structure of the monitored area raises challenges that have not been adequately addressed in the literature to date. In this paper, we identify an appropriate metric to measure the quality of WSN border crossing detection. Furthermore, we propose a method to calculate the required number of sensor nodes to deploy in order to achieve a specified level of coverage according to the chosen metric in a given belt region, while maintaining radio connectivity within the network. Then, we contribute a novel cross layer routing protocol, called levels division graph (LDG), designed specifically to address the communication needs and link reliability for topologically linear WSN applications. The performance of the proposed protocol is extensively evaluated in simulations using realistic conditions and parameters. LDG simulation results show significant performance gains when compared with its best rival in the literature, dynamic source routing (DSR). Compared with DSR, LDG improves the average end-to-end delays by up to 95%, packet delivery ratio by up to 20%, and throughput by up to 60%, while maintaining comparable performance in terms of normalized routing load and energy consumption.

A 0.18 /spl mu/m CMOS implementation of on-chip analogue test signal generation from digital test patterns

The test of analogue and mixed-signal (AMS) cores requires the use of expensive AMS testers and accessibility to internal analogue nodes. The test cost can be considerably reduced by the use of built-in-self-test (BIST) techniques. One of these techniques consists of generating analogue test signals from digital test patterns (obtained via /spl Sigma//spl Delta/ modulation) and converting the responses of the analogue modules into digital signatures that are compared with the expected ones. This paper presents an implementation of the analogue test signal generation part that includes programmability of the circuit blocks, leading to an improvement of performance and a reduction of circuit size with respect to previous approaches. A 0.18 /spl mu/m CMOS circuit has been designed and fabricated, allowing the generation of test signals ranging from 10 Hz to 1 MHz.

CupCarbon: A New Platform for Designing and Simulating Smart-City and IoT Wireless Sensor Networks (SCI-WSN)

The proliferation of radio communication systems and the significant advances in enabling device technologies are paving towards Internet-of-Things (IoT) and opening new horizons for Smart City applications and its services. Such evolution becomes essential in order to enhance quality of urban services, to reduce costs, and to engage citizens more actively. In this context, novel simulation tools are required to prepare the future deployments of large-scale IoT infras tructure for Smart cities in the best conditions in terms of reliability, energy consumption, and cost. This keynote session presents the CupCarbon1 framework: a platform for designing smart-city and IoT Wireless Sensor Networks (SCIWSN). CupCarbon aims to provide following benefits that makes it significant from the other conventional wireless sensor network simulators. (1) provides modeling and simulation of radio propagation channel and alpha-stable noise based interferences in more realistic way, (2) takes into account the deployment environment and quantify the uncertainty of simulations, (3) allows the representation of mobile nodes and dynamic environments, (4) allows the behavioural study of a network or networks with large number of nodes in practical environments (city, mountain, etc.). The CupCarbon simulator allows its user to design, visualize, debug and validate distributed algorithms for monitoring environmental data collections of wireless sensor network. It creates environmental scenarios such as fires, gas, mobiles, and generally within educational and scientific projects. It offers two different simulation environments. First is a multi-agent environment that enables the design of mobility scenarios and the generation of events such as fires and gas as well as the simulation of mobile nodes. Second environment represents a discrete event simulation of wireless sensor networks which also takes into account the scenario designed on the basis of the first environment. Interference models based on the impulsive nature of noise and outdoor propagation models are embedded within Cup-Carbon to provide more realistic analysis of WSNs for smart city applications. These models are associated with spatial zones according to the electromagnetic interactions.

A Distributed Multi-path Routing Algorithm to Balance Energy Consumption in Wireless Sensor Networks

Abstract A large use of applications of Wireless Sensor Networks (WSNs) pushes researchers to design and improve protocols and algorithms against the encountered challenges. One of the main goals is data gathering and routing to the base station (through the sink nodes) with lack of acknowledgement and where each node has no information about the network. Unbalanced energy consumption during the data routing process is an inherent problem in WSNs due to the limited energy capacity of the sensor nodes. In fact, WSNs require load balancing algorithms that make judicious use of the limited energy resource to route the gathered data to the sink node. In this paper, we propose a balanced multi-path routing algorithm by focusing on the residual energy and the hop count of each node to discover the best routes and to insert them into the routing table. The main idea of this algorithm comes from Ant Colony Optimization (ACO) and automata network modelization. Hence, the potential performance of the proposed algorithm relies on the best route to be selected which should have the minimum number of hops, the maximum energy and weighted energy between participating nodes to extend the lifetime of the network.

D-LPCN: A Distributed Least Polar-angle Connected Node Algorithm for Finding the Boundary of a Wireless Sensor Network

A boundary of wireless sensor networks (WSNs) can be used in many fields, for example, to monitor a frontier or a secure place of strategic sensitive sites like oil fields or frontiers of a country. This situation is modeled as the problem of finding a polygon hull in a connected Euclidean graph, which represents a minimal set of connected boundary nodes. In this paper we propose a new algorithm called D-LPCN (Distributed Least Polar-angle Connected Node) which represents the distributed version of the LPCN algorithm introduced in [1]. In each iteration, any boundary node, except the first one, chooses its nearest polar angle node among its neighbors with respect to the node found in the previous iteration. The first starting node can be automatically determined using the Minimum Finding algorithm, which has two main advantages. The first one is that the algorithm works with any type of a connected network, given as planar or not. Furthermore, it takes into account any blocking situation and contains the necessary elements to avoid them. The second advantage is that the algorithm can determine all the boundaries of the different connected parts of the network. The proposed algorithm is validated using the CupCarbon, Tossim and Contiki simulators. It has also been implemented using real sensor nodes based on the TelosB and Arduino/XBee platforms. We have estimated the energy consumption of each node and we have found that the consumption of the network depends on the number of the boundary nodes and their neighbors. The simulation results show that the proposed algorithm is less energy consuming than the existing algorithms and its distributed version is less energy consuming than the centralized version.