Recent Advances in Microelectronics Reliability
Contributions from the European ECSEL JU project iRel40
- 2024
- Book
- Editors
- Willem Dirk van Driel
- Klaus Pressel
- Mujdat Soyturk
- Publisher
- Springer International Publishing
About this book
This book describes the latest progress in reliability analysis of microelectronic products. The content grows out of an EU project, named Intelligent Reliability 4.0 - iRel40 (see www.irel40.eu ). Different industrial sectors and topics are covered, such as electronics in automotive, rail transport, lighting and personal appliances. Several case studies and examples are discussed, which will enable readers to assess and mitigate similar failure cases. More importantly, this book tries to present methodologies and useful approaches in analyzing a failure and in relating a failure to the reliability of electronic devices.
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Table of Contents
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Frontmatter
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Chapter 1. Reliability: Past and Present
W. D. van Driel, K. Pressel, M. SoyturkThe chapter delves into the rich history of reliability concepts in electronics, starting from the 1950s with the advent of standardized stress-based tests. It discusses the evolution through three distinct waves: stress-based, knowledge-based, and application-based testing methodologies. The text highlights the current shift towards the fourth wave of reliability, which focuses on the physics of degradation and robustness validation. These advanced techniques leverage digital twin technology and machine learning to predict failures and optimize maintenance, promising significant improvements in product reliability and cost savings.AI Generated
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AbstractIn this chapter, the past and present situations of the reliability domain are discussed. As of today, most industries are in the transfer from test-to-pass approaches to more advanced strategies. These strategies currently are to determine the reliability capability by applying (where possible) the test-to-failure concept, extending reliability qualification conformance tests beyond the required levels, and assessing any physical or electrical degradation of a product during those tests. New concepts are under investigation that focus on the physics of degradation. Progress in the area of reliability will never stop so as to reduce the amount (and cost) of product field failures. -
Chapter 2. Material Characterization and Modelling for FE-Based Reliability Assessment of PCBs and Electronic Systems
Markus Frewein, Thomas Krivec, Tanja Ortner, Tao Qi, Maike Sagerer, Sebastian Waschnig, Markus Weninger, Julia ZündelThis chapter explores the transition from traditional test-based product generation to simulation-driven design in the electronics industry. It emphasizes the importance of accurate material data for predicting the reliability of PCBs and electronic systems. The authors discuss the limitations of current material data availability and the need for comprehensive, validated models. They provide a detailed overview of material characterization techniques and highlight the significance of temperature-dependent and orthotropic properties. The chapter also includes practical examples and validation studies, demonstrating the superiority of advanced material models in predicting warpage and reliability. This makes the chapter a valuable resource for professionals seeking to enhance their understanding of material modeling and simulation in electronic systems.AI Generated
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AbstractThermo-mechanical reliability of ECS in general and PCBs in particular is mainly driven by the physical and mechanical behaviour of the applied materials and the mismatch in the properties of those materials. Thus, to create products that feature reliability by design, a detailed know-how of material properties is required mandatorily. This chapter provides an overview on the current situation regarding materials and the availability of material data for base materials of PCBs. A suggestion for data to be considered for material models that are suitable for FEA-based reliability predictions is provided and discussed. A set of (test) methods for characterization of the required material properties data is proposed. In order to substantiate the need of advanced and representative material data, a comparison of the quality of the results from two different FEA is presented. Based on a ten-layer HDI board for a “Machine to X” communication module (M2X module), cases for warpage after press and lifetime under cyclic load in hot oil test conditions have been calculated. While the first setup featured literature−/data sheet-based material models, this is compared against a setting with advanced material models featuring temperature-dependent, characterized and fitted material data. The results of the jobs were validated based on demonstrator boards of the respective M2X module board that have been built and analysed for warpage. Additionally, a hot oil test has been carried out for the bare boards. The test results strongly support the requirement for advanced material models for finite elements simulations. Finally, an outlook on needed future activities in the area of material properties and material data for FEA is given. -
Chapter 3. Smart Optical Inline Metrology
Christopher Taudt, Alexander Kabardiadi-Virkovski, Tobias Baselt, Karsten Schmiedel, Peter HartmannThe chapter delves into the electronics industry's shift toward the More-than-Moore paradigm, emphasizing the need for advanced metrology to keep pace with complex semiconductor manufacturing. It introduces High-Dynamic Range Profilometry using Spectral Imaging Interferometry, a novel approach for high-precision surface profiling. The DE-LCI measurement principle and its experimental setup are detailed, showcasing its ability to handle large measurement ranges and high speeds. The chapter also explores the resolution and measurement range of this approach, along with its qualification and measurement results on height standards. Additionally, it discusses the potential for 3D measurements and tomographic capabilities, highlighting the versatility of the DE-LCI approach. The chapter concludes with insights into inline integration, demonstrating the practical application of this advanced metrology in industrial settings.AI Generated
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AbstractThis chapter outlines modern approaches for inline metrology which helps to increase reliability of manufacturing processes and products. A major emphasis is placed on two specific metrology techniques. Also, there will be an introduction of important system components such as the data handling infrastructure and specifically developed light sources. In order to cope with high demands on surface texture, defect, and critical dimension control, a high-dynamic range interferometric technology is presented which enables sub-nm resolution on \({\upmu }\)m-sized features. The detection of residuals and contamination is performed with a specialized approach to laser-breakdown spectroscopy with high temporal and spectral resolution. These technologies are enabled by novel, super-continuum-based light sources which deliver new levels of brilliance, temporal control, and power. The conception, implementation, and characterization of these light sources are demonstrated. In order to facilitate highly integrated metrology, appropriate dataflow chains and infrastructure models are introduced and discussed. -
Chapter 4. Automated Classification of Semiconductor Defect Density SEM Images Using Deep Learning
Corinna Kofler, Francisco López de la Rosa, Dominic Zarre, Gianluca Guglielmo, Claudia Anna Dohr, Judith Dohr, Anja Zernig, Antonio Fernández-CaballeroThe chapter delves into the automated classification of semiconductor defect density SEM images using deep learning. It introduces the need for automated classification to improve wafer reliability and outlines the data collection, preprocessing, and model training processes. The authors present two datasets, Carinthia and Madrid, each with unique challenges such as class imbalance. They explore various data augmentation techniques and CNN architectures, including ResNet50 and EfficientNet, to address these challenges. The chapter also discusses the deployment of the models in a production environment and the implementation of a productive check to handle new defect types. The results showcase the effectiveness of the data-centric approach and the potential for further improvements in model performance and automation.AI Generated
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AbstractThe early detection of defect density issues is a crucial part of product reliability in the semiconductor manufacturing industry, since it prevents failures of the final product. To assure reliability numerous inspections are performed. One of these inspections involves the classification of defects on wafers using images captured with scanning electron microscopes. Currently, experts perform manual classification of these images. However, this task is susceptible to errors and is not efficient. The goal of this work was to increase the reliability of the classification by developing a deep learning pipeline, using convolutional neural networks, for automatic defect classification. The basis for training the models is a set of historical images stored in the production’s database. For this work, we extracted from the database one less complex dataset and one complex dataset and applied data preparation methods to them to create our training datasets. The less complex dataset contains a few classes of defect images of one technology and one inspection step. The complex dataset contains a multitude of classes of defect images of different technologies and inspection steps. We performed and evaluated model-centric experiments on the less complex dataset, which we refer to as the Carinthia dataset, and we performed data-centric experiments on the complex dataset, which we refer to as Madrid dataset. Furthermore, we deployed the best model trained on the complex Madrid dataset, which achieved a validation accuracy of 92.7% for productive usage. -
Chapter 5. An Artificial Intelligence-Based Framework for Burn-in Reduction in the Semiconductor Manufacturing Industry
Ibrahim Ahmed, Fatemeh Hosseinpour, Piero Baraldi, Enrico Zio, Horst LewitschnigThe chapter introduces an innovative AI-based framework designed to reduce burn-in costs in the semiconductor manufacturing industry. By leveraging data from production machinery, wafer maps, and electrical diagnostics tests, the framework employs advanced machine learning techniques such as LSTM-based CNN, PCA-OCSVM, and SVR to predict device quality before traditional burn-in testing. The proposed methods aim to optimize burn-in policies, thereby reducing both cost and time. The chapter also highlights the potential of this framework to inform critical decisions in the manufacturing process, enhancing overall device reliability and customer acceptance of new technologies.AI Generated
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AbstractIn the semiconductor manufacturing industry, burn-in is performed to screen out latent defects, which can cause failures during early-life stages. However, burn-in is expensive and time-consuming, since it requires extensive testing under accelerated stress conditions, such as high temperature. As early-life failures are originated during the different stages of the manufacturing process, we develop a framework to estimate the quality of a production lot by exploiting different sources of data collected from different production machines. Specifically, we consider (i) signals measured from the machines used in semiconductor production; (ii) wafer map images collected by performing probe tests on the dies of the processed wafers; and (iii) results of electrical tests performed prior to burn-in. With regard to the exploitation of the data in (i), a method for detecting anomalies of the most critical production machines based on long short-term memory (LSTM)-based convolutional neural network (CNN) has been developed. With regard to the exploitation of the data in (ii) and (iii), two methods for predicting the production quality have been developed. The former is based on the combination of principal component analysis (PCA) and one-class support vector machine (OCSVM), whereas the latter is based on support vector regression (SVR). The results of the applications of the developed methods to real production data show that the quality of the production can be effectively estimated and, therefore, used for a data-informed decision on the number and type of burn-in tests to be performed. -
Chapter 6. Early Lifetime Estimation for Automotive LIDAR Using Realistic L4 Usage Profiles
Pamela Innerwinkler, Stephanie Grubmüller, Horst Lewitschnig, Marlies Mischinger-Rodzievicz, Nidhi BalajiThe chapter explores the critical role of environmental factors such as ambient temperature, solar radiation, and route conditions in determining the reliability and useful life of automotive LIDAR systems. It introduces a framework for L4AD! drive cycles combined with ambient temperature and solar irradiance to construct unique 24-hour usage profiles. The authors employ Support Vector Regression (SVR) to model the LIDAR system temperature, enabling a more accurate estimation of the burn-in (BI) phase. This model is then applied to relate BI time to operational performance, providing a robust method for predicting the lifetime of LIDAR systems in real-world applications. The chapter concludes with a discussion on future work, highlighting the need for further measurements and model refinements to enhance the precision of lifetime estimations.AI Generated
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AbstractA method is presented that relates the BI! (BI!) phase, where a temperature stress is applied to an electronic component after manufacturing, to the L4AD! (L4AD!) operational phase. Relating the BI! phase to the performance in the field requires an in-depth investigation of the temperature of the component. For a more precise estimation of the temperature of the system and furthermore the junction temperature, a model based on SVR! (SVR!) representing the system temperature of a LIDAR! (LIDAR!) sensor is determined. Depending on this model, an average temperature behaviour of the component over a year is approximated utilizing annual usage profiles including weather information and duty cycles. This temperature profile is then applied to the estimation of the burn-in time allocated to the L4AD! application, and the outcomes are compared to the results of the burn-in time estimation in L4AD! application with assumed constant system temperature. -
Chapter 7. Improving the Reliability of Automotive Systems
Jose Ángel GumielThe automotive industry is undergoing a disruptive period with the integration of electronics into mechanical components, driven by the demand for advanced features and increased vehicle safety. This chapter delves into the challenges faced by Tier 1 suppliers, who must meet stringent quality, safety, and environmental standards while competing for contracts with OEMs. It highlights the importance of reliability in automotive systems and the complexities introduced by the transition from mechanics to electronics. The chapter also discusses the increasing role of mechatronics, the convergence of mechanical, electrical, and computer science, and the need for digitalization in mechanical engineering. Additionally, it emphasizes the critical aspect of cybersecurity in modern vehicles, which has become a significant concern with the rise of connected cars. The chapter provides insights into the standards and guidelines that must be followed to ensure the safety and reliability of electronic components in automotive applications.AI Generated
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AbstractThe automotive industry is changing rapidly with technological advances and a focus on greener and safer vehicles. Original equipment manufacturers (OEMs) are pushing for electronic innovation. At the same time, suppliers, many of whom focus solely on mechanical products, are urged to incorporate electronics into their portfolios to avoid commoditization. Successfully meeting this challenge offers the potential for differentiation and increased value but also opens the door to competition from technology companies looking to enter the industry.The automotive industry is adapting to change and facing new challenges while building on past experience. With the increasing interaction of electronics in vehicles, ensuring the functional safety of mechatronic systems is critical. Accurate manufacturing and repeatability are essential, as is the alignment of electronic and mechanical components for exact measurements and movements. Similarly, software development requires procedures for verifying and validating behavior to avoid bugs and enable secure updates when needed.The integration of electronics into vehicles has raised cybersecurity concerns. This raises questions about how to ensure the safety and security of cars, what to do when vulnerabilities are discovered, and how long a vehicle should receive updates. In addition, using artificial intelligence-based systems in vehicles presents new challenges. As these systems are “black boxes,” ensuring their reliability and safety is more complicated.OEMs and TIER 1 suppliers must comply with standards and regulations to ensure the reliability of automotive systems. Achieving compliance requires significant time, resources, and changes to internal processes, but ensuring a safe, reliable, and cyber-secure fleet is essential. This chapter provides an overview of relevant standards, guidelines, and recommendations, proposing the Automotive SPICE framework as a tool for effectively implementing them within an organization. -
Chapter 8. Reliability Improvements for In-Wheel Motor
Gašper Petelin, Rok Hribar, Stanko Ciglarič, Jernej Herman, Anton Biasizzo, Peter Korošec, Gregor PapaThis chapter delves into the critical need for reliability improvements in in-wheel electric motors, which are gaining traction in the automotive industry. It introduces an intelligent condition monitoring system that leverages AI algorithms to predict insulation aging and enhance the overall durability of these motors. The research involves developing a customized measuring device that is both cost-effective and accurate, thanks to AI-assisted signal processing. The chapter also describes extensive testing sessions to evaluate the performance of this device compared to commercially available standards. Predictive algorithms, such as random forest and XGBoost, are employed to predict insulation resistance with high precision. The findings indicate that temperature and humidity are crucial factors influencing the performance of these predictive models. This research not only aims to improve the reliability of electric motors but also paves the way for predictive maintenance strategies in electric vehicles, highlighting the potential for future advancements in the field.AI Generated
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AbstractSetting up a reliable electric propulsion system in the automotive sector requires an intelligent condition monitoring device capable of reliably assessing the state and the health of the electric motor. To allow for a massive integration of such monitoring devices, they must be inexpensive and small. These requirements limit their accuracy. However, we show in this chapter that these limitations can be significantly reduced by appropriate processing of the sensor data. We have used machine learning models (random forest and XGBoost) to transform very noisy motor winding insulation resistance measurements made by a low-cost device into a much more reliable value that can compete with measurements made by a high-priced state-of-the-art measurement system. The proposed method is an important building block for a future smart condition monitoring system and enables a cost-effective and accurate assessment of the condition of electric motor health in connection with the condition of their winding insulation. -
Chapter 9. Big Data Streaming and Data Analytics Infrastructure for Efficient AI-Based Processing
Fatima tu Zahra, Yavuz Selim Bostanci, Ozay Tokgozlu, Malik Turkoglu, Mujdat SoyturkThis chapter delves into the intricate relationship between big data and AI, focusing on the challenges and opportunities presented by the exponential growth of digital data. It introduces the concept of big data, its characteristics, and life cycle, emphasizing the need for robust infrastructure to manage and analyze vast datasets. The chapter explores the role of AI in optimizing data streaming and analytics, enabling real-time processing and decision-making. It also discusses the integration of AI with big data in various industries, such as healthcare, finance, and manufacturing, showcasing how these technologies drive innovation and enhance operational efficiencies. Additionally, the chapter highlights the importance of data quality, security, and governance in AI applications, addressing the ethical and sustainability considerations in data processing. The future trends in big data and AI are also explored, emphasizing the potential of artificial general intelligence, quantum computing, and edge AI in reshaping the digital landscape. The chapter concludes by emphasizing the responsible and sustainable use of these technologies for societal advancement and global development.AI Generated
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AbstractThe exponential growth of data in this digital age has necessitated advanced methodologies to effectively manage, process, and analyze vast datasets. The convergence of big data and AI presents a transformative opportunity across various sectors, yet it poses unique challenges in managing the scale, speed, and complexity of data. This chapter provides an extensive overview of big data streaming and data analytics, focusing on the critical aspect of appropriate infrastructure for efficient AI-based processing. It explains the core concepts of big data, its inherent challenges, and how it impacts AI model efficiency, accuracy, and reliability. It provides an in-depth look at the tools and technological frameworks involved in data streaming and analytical processes. Additionally, the infrastructure requirements for supporting the intensive computational demands of AI are also examined. It also presents effective techniques for data monitoring and visualization to maximize insights. The discussion then shifts toward the integration of AI in big data analytics, illustrating how machine learning and predictive analytics can significantly improve real-time decision-making processes. The emerging trends and future directions are also explored along with the ethical, privacy, and sustainability considerations, elaborating on the importance of responsible and conscious development in AI and big data analytics. -
Chapter 10. An Outlook on Power Electronics Reliability and Reliability Monitoring
Henry A. Martin, Edsger C. P. Smits, R. H. Poelma, Willem D. van Driel, G. Q. ZhangThe chapter delves into the critical role of reliability in power electronics, highlighting the challenges and transformative shifts in reliability qualification. It explores dominant degradation mechanisms affecting power devices and introduces advanced measurement techniques for monitoring device performance, including thermal imaging and temperature-sensitive electrical parameters. The chapter also discusses the evolving landscape of reliability metrics and the importance of online reliability monitoring for sustainable future systems.AI Generated
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AbstractThe increasing awareness of environmental concerns and sustainability underlines the importance of energy-efficient systems, renewable energy technologies, electric vehicles, and smart grids. Hence, stringent constraints and safety regulations have been prompted to meet reliability standards in power electronics. This chapter provides a comprehensive outlook on the current state of power semiconductor devices, field-critical applications, dominant degradation mechanism (chip-related and package-related), and the emerging measurement techniques for reliability/condition monitoring. This chapter delves into the underlying physics behind each reliability measurement method reviewed. A comparative summary of cost, complexity, online monitoring capability, accuracy, and intrusiveness is provided to enable readers to make informed decisions about the measurement methods. This chapter emphasizes the significance of early fault detection through online monitoring, as it can effectively reduce system downtime for seamless non-interruptive operation. -
Chapter 11. Digital Twin Technology in Electronics
H. Moeller, A. Inamdar, W. D. van Driel, J. Bredberg, P. Hille, H. Knoll, B. VandeveldeThe chapter delves into the concept of Digital Twin technology, its evolution, and its application in the electronics industry. It discusses the use of Digital Twins to enhance product reliability and performance, with a focus on the Digital Twin for electronic components and systems (ECS). The chapter provides an overview of the Digital Twin's structure, the challenges in its implementation, and the benefits it brings to the product development and maintenance process. It also includes practical use cases, such as the Digital Twin for a current measurement device and a DC/DC converter in a Scania truck, to illustrate the real-world applications of this technology. The chapter emphasizes the importance of Digital Twin technology in the era of Industry 4.0 and its potential to revolutionize the way products are designed, manufactured, and maintained.AI Generated
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AbstractThe main driver behind the wide adoption of the Digital Twin concept is the increasing digitalization of development and manufacturing processes combined with the demand to monitor and influence the product behavior along its whole life cycle which has brought Digital Twin concept to where it is today. In this chapter, we have described the Digital Twin technology by itself and provided a series of use case demonstrating its wider use. -
Chapter 12. A Framework for Applying Data-Driven AI/ML Models in Reliability
Rok Hribar, Margarita Antoniou, Gregor PapaThe chapter introduces a framework for applying data-driven AI/ML models to enhance reliability in electronics, a critical aspect affecting efficiency and productivity. It discusses the importance of reliability in reducing costs and improving system performance, and how AI/ML models can predict and prevent failures through real-time monitoring and predictive maintenance. The framework, developed within the iRel40 project, aims to reduce failure rates and enhance knowledge in the field of reliability. It covers various AI techniques, their applications in diagnostics and prognostics, and the challenges and solutions encountered in real-world use cases. The chapter also provides guidelines for choosing appropriate AI methodologies and models based on problem and data characteristics, making it a valuable resource for professionals seeking to implement AI/ML in reliability engineering.AI Generated
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AbstractIn this chapter, we present a framework for applying artificial intelligence (AI)/machine learning (ML) in reliability, in the context of the iRel40 project. Data-driven models are becoming an increasingly fruitful tool for detecting patterns in complex data and identifying the circumstances in which they occur. Using only data, gathered along the value chain, data-driven methods are now being used to detect indications of potential early failures, signs of wear out or degradation, and other unwanted events within the development, fabrication, or service phases of the electronic components and systems. We present general considerations that were found to be important during the iRel40 project, when designing pipelines that combine data processing with the AI/ML models for predicting or detecting reliability issues. This chapter serves as an introduction to the definitions and concepts used within the specific use cases that rely on the AI/ML methodology within the iRel40 project. -
Chapter 13. Health Monitoring Fatigue Properties of Solder Interconnects in LED Drivers
L. Du, X. Zhao, R. H. Poelma, W. D. van Driel, G. Q. ZhangThe chapter delves into the critical issue of solder joint reliability in LED drivers, emphasizing the impact of potting materials and creep behavior under extreme conditions. It combines experimental characterization of solder materials with finite element simulations to develop a prognostics and health monitoring (PHM) methodology. The study reveals how potting compounds can accelerate fatigue failure and presents a model for predicting the remaining lifetime of solder interconnects. The chapter also highlights the importance of understanding the creep properties of solder alloys and the effects of potting materials on thermal fatigue properties. By integrating experimental data with advanced numerical simulations, the chapter offers valuable insights into enhancing the reliability of electronic components in harsh environments.AI Generated
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AbstractSolder fatigue is a key failure mode in the electronic industry. Monitoring the actual degradation of the solder under real-time conditions in any application would be extremely beneficial. In this chapter, we describe the combination of experimental material characterization with numerical finite element (FE) simulations to obtain a prognostics and health monitoring (PHM) methodology for LED drivers used in outdoor lighting applications. Experimental characterization of a new type of solder is described. A FE model is created of a typical component in electronic drivers. The calculated damage level and the collected life data correlate together and form a model for predicting the lifetime of the drivers at certain user condition. The developed PHM methodology helps in identifying and reporting the failure of the driver in real time or can be used for predicting the actual remaining useful life (RUL). -
Chapter 14. Executing Condition Monitoring Algorithms on ARM Cortex-M4 Using Tensorflow Lite for Microcontrollers
Manfred Mücke, Christoph SieglThe chapter delves into the challenges and solutions for executing condition monitoring algorithms on resource-constrained ARM Cortex-M4 microcontrollers using TensorFlow Lite for Microcontrollers (TFLM). It begins by introducing the components and workflow of the TensorFlow ecosystem, including TensorFlow, TensorFlow Lite, and TFLM. The experimental setup involves training and executing three types of machine learning models—convolutional neural networks (CNN), transformers, and long short-term memory (LSTM) networks—on the Arduino Nano 33 BLE Sense development board. The chapter highlights the limitations and compatibility issues of these models with TFLM, focusing on the CNN model as the only successful execution. It also provides a detailed analysis of memory consumption and execution times, comparing the performance of TFLM with TensorFlow Lite on both PC and Arduino Nano. The discussion concludes with recommendations for improving the TensorFlow ecosystem's reliability and performance in such constrained environments.AI Generated
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AbstractCondition monitoring systems (CMS) require execution of inverse models of physical systems close to the product to estimate the system’s status from sensor readings. This requires mapping and executing elaborate numerical models—potentially including machine learning models—onto computing devices with limited power budget, limited memory and limited support for floating-point arithmetic. Most existing ML frameworks provide only limited support for this task. We document in detail a workflow using Tensorflow Lite for Microcontrollers (TFLM) to map an LSTM, CNN and transformer model onto an Arduino Nano, featuring an ARM Cortex-M4 CPU. We discuss identified shortcomings and their implications on CMS design. -
Chapter 15. Design Support for Reliable Integrated Circuits
Sonja Crocoll, André LangeThis chapter delves into the critical aspects of designing reliable integrated circuits (ICs), emphasizing the importance of Design for Reliability (DfR) methods. It covers the complete design flow, from technology and device selection to design verification, highlighting tools like RelXplorer and ReliaVision that enable lifetime predictions and reliability assessments. The chapter also discusses aging mechanisms such as Hot Carrier Injection (HCI) and Bias Temperature Instability (BTI), and presents a case study demonstrating the application of these tools to a low-dropout regulator circuit. By integrating reliability considerations into the design process, the chapter aims to enhance the longevity and robustness of ICs, ultimately leading to more reliable products and reduced development cycles.AI Generated
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AbstractIntegrated circuits (ICs) are the basis for almost all microelectronic applications independent of their particular target market segments. Therefore, to make microelectronic systems reliable, ICs should be reliable analogously. This chapter presents methods and approaches to take IC reliability into account in design projects. In particular, it discusses possibilities to consider reliability information for selecting the most suitable semiconductor technology and devices to apply in a particular product. Furthermore, it introduces how transistor degradation due to hot carrier injection (HCI) and bias temperature instability (BTI) can be investigated and handled in transistor-level circuit design. -
Chapter 16. Outlook to the Future of Reliability
W. D. van Driel, K. Pressel, M. Soyturk, H. Knoll, P. HilleThe chapter 'Outlook to the Future of Reliability' delves into the intricacies of reliability engineering, distinguishing it from quality over time. It discusses critical to reliability parameters (CTR) and their link to critical to quality parameters (CTQ), using product recalls and the cost of non-quality (CoNQ) as key measures. The text highlights significant recalls, such as the Boeing 737 MAX and computer battery incidents, emphasizing the substantial financial and reputational costs of reliability flaws. It also explores the use of multi-scale and multi-physics simulations for predicting electronics reliability, emphasizing the need for breakthrough developments in these areas. The chapter further discusses smarter testing and characterization methods, the embedding of AI in design for reliability, and the importance of prognostics and health management (PHM) for predicting remaining useful life (RUL). It concludes by emphasizing the bright future of reliability, driven by advancements in simulation, testing, and data analytics.AI Generated
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AbstractThe future of reliability is bright as each electronics company is spending 1–5% of their annual sales on cost of non-quality. These costs are related to product failures prior to the end of its warranty period. If even a portion of these costs are reduced, it would result in a substantial profit increase. Progress in the area of reliability can be found in five directions; (i) multi-scale and multi-physics simulations for physics of degradation; (ii) AI-based control systems in advanced production; (iii) smart sensoring and big data analysis; (iv) reliable materials and reliability testing; and (v) prognostics and health management/digital twin for condition monitoring. In this chapter, we discuss these directions and what it would mean for the future developments of the reliability domain. -
Backmatter
- Title
- Recent Advances in Microelectronics Reliability
- Editors
-
Willem Dirk van Driel
Klaus Pressel
Mujdat Soyturk
- Copyright Year
- 2024
- Publisher
- Springer International Publishing
- Electronic ISBN
- 978-3-031-59361-1
- Print ISBN
- 978-3-031-59360-4
- DOI
- https://doi.org/10.1007/978-3-031-59361-1
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