Reliability Analysis of Electrotechnical Devices

This is a book on the practical approaches of reliability to electrotechnical devices and systems. It includes the electromagnetic effect, radiation effect, environmental effect, and the impact of the manufacturing process on electronic materials, devices, and boards

Heurísticas bioinspiradas para el problema de Floorplanning 3D térmico de dispositivos MPSoCs

Tesis inédita de la Universidad Complutense de Madrid, Facultad de Informática, Departamento de Arquitectura de Computadores y Automática, leída el 20-06-2013Depto. de Arquitectura de Computadores y AutomáticaFac. de InformáticaTRUEunpu

A deep learning-based approach for defect classification with context information in semiconductor manufacturing

This thesis presents some methodological and experimental contributions to a deep learning-based approach for the automatic classifi cation of microscopic defects in silicon wafers with context information. Canonical image classifi cation approaches have the limitation of utilizing only the information contained in the images. This work overcomes this limitation by using some context information about the defects to improve the current automatic classifi cation system

A Survey on Reservoir Computing and its Interdisciplinary Applications Beyond Traditional Machine Learning

Reservoir computing (RC), first applied to temporal signal processing, is a recurrent neural network in which neurons are randomly connected. Once initialized, the connection strengths remain unchanged. Such a simple structure turns RC into a non-linear dynamical system that maps low-dimensional inputs into a high-dimensional space. The model's rich dynamics, linear separability, and memory capacity then enable a simple linear readout to generate adequate responses for various applications. RC spans areas far beyond machine learning, since it has been shown that the complex dynamics can be realized in various physical hardware implementations and biological devices. This yields greater flexibility and shorter computation time. Moreover, the neuronal responses triggered by the model's dynamics shed light on understanding brain mechanisms that also exploit similar dynamical processes. While the literature on RC is vast and fragmented, here we conduct a unified review of RC's recent developments from machine learning to physics, biology, and neuroscience. We first review the early RC models, and then survey the state-of-the-art models and their applications. We further introduce studies on modeling the brain's mechanisms by RC. Finally, we offer new perspectives on RC development, including reservoir design, coding frameworks unification, physical RC implementations, and interaction between RC, cognitive neuroscience and evolution.Comment: 51 pages, 19 figures, IEEE Acces

Ferrite-based micro-inductors for power systems on chip : from material elaboration to inductor optimisation

Les composants passifs intégrés sont des éléments clés pour les futures alimentations sur puce, compactes et présentant des performances améliorées: haut rendement et forte densité de puissance. L'objectif de ce travail de thèse est d'étudier les matériaux et la technologie pour réaliser de bobines à base de ferrite, intégrées sur silicium, avec des faibles empreintes (<4 mm ²) et de faible épaisseur (<250 µm). Ces bobines, dédiées à la conversion de puissance (˜ 1 W) doivent présenter une forte inductance spécifique et un facteur de qualité élevé dans la gamme de fréquence visée (5-10 MHz). Des ferrites de NiZn ont été sélectionnées comme matériaux magnétiques pour le noyau des bobines en raison de leur forte résistivité et de leur perméabilité stable dans la gamme de fréquence visée. Deux techniques sont développées pour les noyaux de ferrite: la sérigraphie d'une poudre synthétisée au laboratoire et la découpe automatique de films de ferrite commerciaux, suivi dans chaque cas du frittage et le placement sur les conducteurs pour former une bobine rectangulaire. Des bobines tests ont été réalisées dans un premier temps afin que la caractérisation puisse être effectuée : les propriétés magnétiques du noyau de ferrite notamment les pertes volumiques dans le noyau sont ainsi extraites. L'équation de Steinmetz a permis de corréler les courbes de pertes mesurées avec des expressions analytiques en fonction de la fréquence et de l'induction. La deuxième phase de la thèse est l'optimisation de la conception de la micro-bobine à base de ferrite, en tenant compte des pertes attendues. L'algorithme générique est utilisé pour optimiser les dimensions de la bobine avec pour objectif ; la minimisation des pertes et l'obtention de la valeur d'inductance spécifique souhaitée, sous faible polarisation en courant. La méthode des éléments finis pour le magnétisme FEMM est utilisée pour modéliser le comportement électromagnétique du composant. La deuxième série de prototypes a été réalisée afin de valider la méthode d'optimisation. En perspective, les procédés de photolithographie de résine épaisse et le dépôt électrolytique sont en cours de développement pour réaliser les enroulements de cuivre épais autour des noyaux de ferrite optimisés et ainsi former le composant complet.On-chip inductors are key passive elements for future power supplies on chip (PwrSoC), which are expected to be compact and show enhanced performance: high efficiency and high power density. The objective of this thesis work is to study the material and technology to realize small size (<4 mm²) and low profile (< 250 µm) ferrite-based on-chip inductor. This component is dedicated to low power conversion (˜ 1 W) and should provide high inductance density and high quality factor at medium frequency range (5-10 MHz). Fully sintered NiZn ferrites are selected as soft magnetic materials for the inductor core because of their high resistivity and moderate permeability stable in the frequencies range of interest. Two techniques are developed for the ferrite cores: screen printing of in-house made ferrite powder and cutting of commercial ferrite films, followed in each case by sintering and pick-and place assembling to form the rectangular toroid inductor. Test inductors were realized first so that the characterization could be carried out to study the magnetic properties of the ferrite core and the volumetric core losses. The core losses were fit from the measured curve with Steinmetz equation to obtain analytical expressions of losses versus frequency and induction. The second phase of the thesis is the design optimization for the on-chip ferrite based inductor, taking into account the expected losses. Genetic algorithm is employed to optimize the inductor design with the objective function as minimum losses and satisfying the specification on the inductance values under weak current-bias condition. Finite element method for magnetics FEMM is used as a tool to calculate inductance and losses. The second run of prototypes was done to validate the optimization method. In perspective, processes of thick-photoresist photolithography and electroplating are being developed to realize the completed thick copper windings surrounding ferrite cores

Design Space Exploration and Resource Management of Multi/Many-Core Systems

The increasing demand of processing a higher number of applications and related data on computing platforms has resulted in reliance on multi-/many-core chips as they facilitate parallel processing. However, there is a desire for these platforms to be energy-efficient and reliable, and they need to perform secure computations for the interest of the whole community. This book provides perspectives on the aforementioned aspects from leading researchers in terms of state-of-the-art contributions and upcoming trends

A Review and Analysis of Automatic Optical Inspection and Quality Monitoring Methods in Electronics Industry

Electronics industry is one of the fastest evolving, innovative, and most competitive industries. In order to meet the high consumption demands on electronics components, quality standards of the products must be well-maintained. Automatic optical inspection (AOI) is one of the non-destructive techniques used in quality inspection of various products. This technique is considered robust and can replace human inspectors who are subjected to dull and fatigue in performing inspection tasks. A fully automated optical inspection system consists of hardware and software setups. Hardware setup include image sensor and illumination settings and is responsible to acquire the digital image, while the software part implements an inspection algorithm to extract the features of the acquired images and classify them into defected and non-defected based on the user requirements. A sorting mechanism can be used to separate the defective products from the good ones. This article provides a comprehensive review of the various AOI systems used in electronics, micro-electronics, and opto-electronics industries. In this review the defects of the commonly inspected electronic components, such as semiconductor wafers, flat panel displays, printed circuit boards and light emitting diodes, are first explained. Hardware setups used in acquiring images are then discussed in terms of the camera and lighting source selection and configuration. The inspection algorithms used for detecting the defects in the electronic components are discussed in terms of the preprocessing, feature extraction and classification tools used for this purpose. Recent articles that used deep learning algorithms are also reviewed. The article concludes by highlighting the current trends and possible future research directions.Framework of the IQONIC Project; European Union’s Horizon 2020 Research and Innovation Program

Using MapReduce Streaming for Distributed Life Simulation on the Cloud

Distributed software simulations are indispensable in the study of large-scale life models but often require the use of technically complex lower-level distributed computing frameworks, such as MPI. We propose to overcome the complexity challenge by applying the emerging MapReduce (MR) model to distributed life simulations and by running such simulations on the cloud. Technically, we design optimized MR streaming algorithms for discrete and continuous versions of Conway’s life according to a general MR streaming pattern. We chose life because it is simple enough as a testbed for MR’s applicability to a-life simulations and general enough to make our results applicable to various lattice-based a-life models. We implement and empirically evaluate our algorithms’ performance on Amazon’s Elastic MR cloud. Our experiments demonstrate that a single MR optimization technique called strip partitioning can reduce the execution time of continuous life simulations by 64%. To the best of our knowledge, we are the first to propose and evaluate MR streaming algorithms for lattice-based simulations. Our algorithms can serve as prototypes in the development of novel MR simulation algorithms for large-scale lattice-based a-life models.https://digitalcommons.chapman.edu/scs_books/1014/thumbnail.jp

Reinforcement Learning Approach for Autonomous UAV Navigation in 3D Space

In the last two decades, the rapid development of unmanned aerial vehicles (UAVs) resulted in their usage for a wide range of applications. Miniaturization and cost reduction of electrical components have led to their commercialization, and today they can be utilized for various tasks in an unknown environment. Finding the optimal path based on the start and target pose information is one of the most complex demands for any intelligent UAV system. As this problem requires a high level of adaptability and learning capability of the UAV, the framework based on reinforcement learning is proposed for the localization and navigation tasks. In this paper, Q-learning algorithm for the autonomous navigation of the UAV in 3D space is implemented. To test the proposed methodology for UAV intelligent control, the simulation is conducted in ROS-Gazebo environment. The obtained simulation results have shown that the UAV can reach the target pose autonomously in an efficient way

Reinforcement Learning Approach for Autonomous UAV Navigation in 3D Space

In the last two decades, the rapid development of unmanned aerial vehicles (UAVs) resulted in their usage for a wide range of applications. Miniaturization and cost reduction of electrical components have led to their commercialization, and today they can be utilized for various tasks in an unknown environment. Finding the optimal path based on the start and target pose information is one of the most complex demands for any intelligent UAV system. As this problem requires a high level of adaptability and learning capability of the UAV, the framework based on reinforcement learning is proposed for the localization and navigation tasks. In this paper, Q-learning algorithm for the autonomous navigation of the UAV in 3D space is implemented. To test the proposed methodology for UAV intelligent control, the simulation is conducted in ROS-Gazebo environment. The obtained simulation results have shown that the UAV can reach the target pose autonomously in an efficient way