Krishnendu Chakrabarty

This book provides an insightful guide to the design, testing and optimization of micro-electrode-dot-array (MEDA) digital microfluidic biochips. The authors focus on the characteristics specific for MEDA biochips, e.g., real-time sensing and advanced microfluidic operations like lamination mixing and droplet shape morphing. Readers will be enabled to enhance the automated design and use of MEDA and to develop a set of solutions to facilitate the full exploitation of design complexities that are possible with standard CMOS fabrication techniques. The book provides the first set of design automation and test techniques for MEDA biochips. The methods described in this book have been validated using fabricated MEDA biochips in the laboratory. Readers will benefit from an in-depth look at the MEDA platform and how to combine microfluidics with software, e.g., applying biomolecular protocols to software-controlled and cyberphysical microfluidic biochips.

This book provides a comprehensive set of characterization, prediction, optimization, evaluation, and evolution techniques for a diagnosis system for fault isolation in large electronic systems. Readers with a background in electronics design or system engineering can use this book as a reference to derive insightful knowledge from data analysis and use this knowledge as guidance for designing reasoning-based diagnosis systems. Moreover, readers with a background in statistics or data analytics can use this book as a practical case study for adapting data mining and machine learning techniques to electronic system design and diagnosis. This book identifies the key challenges in reasoning-based, board-level diagnosis system design and presents the solutions and corresponding results that have emerged from leading-edge research in this domain. It covers topics ranging from highly accurate fault isolation, adaptive fault isolation, diagnosis-system robustness assessment, to system performance analysis and evaluation, knowledge discovery and knowledge transfer. With its emphasis on the above topics, the book provides an in-depth and broad view of reasoning-based fault diagnosis system design.• Explains and applies optimized techniques from the machine-learning domain to solve the fault diagnosis problem in the realm of electronic system design and manufacturing;• Demonstrates techniques based on industrial data and feedback from an actual manufacturing line;• Discusses practical problems, including diagnosis accuracy, diagnosis time cost, evaluation of diagnosis system, handling of missing syndromes in diagnosis, and need for fast diagnosis-system development.

This book provides a comprehensive set of optimization and prediction techniques for an enterprise information system. Readers with a background in operations research, system engineering, statistics, or data analytics can use this book as a reference to derive insight from data and use this knowledge as guidance for production management. The authors identify the key challenges in enterprise information management and present results that have emerged from leading-edge research in this domain. Coverage includes topics ranging from task scheduling and resource allocation, to workflow optimization, process time and status prediction, order admission policies optimization, and enterprise service-level performance analysis and prediction. With its emphasis on the above topics, this book provides an in-depth look at enterprise information management solutions that are needed for greater automation and reconfigurability-based fault tolerance, as well as to obtain data-driven recommendations for effective decision-making.

This book provides a comprehensive set of characterization, prediction, optimization, evaluation, and evolution techniques for a diagnosis system for fault isolation in large electronic systems. Readers with a background in electronics design or system engineering can use this book as a reference to derive insightful knowledge from data analysis and use this knowledge as guidance for designing reasoning-based diagnosis systems. Moreover, readers with a background in statistics or data analytics can use this book as a practical case study for adapting data mining and machine learning techniques to electronic system design and diagnosis. This book identifies the key challenges in reasoning-based, board-level diagnosis system design and presents the solutions and corresponding results that have emerged from leading-edge research in this domain. It covers topics ranging from highly accurate fault isolation, adaptive fault isolation, diagnosis-system robustness assessment, to system performance analysis and evaluation, knowledge discovery and knowledge transfer. With its emphasis on the above topics, the book provides an in-depth and broad view of reasoning-based fault diagnosis system design.• Explains and applies optimized techniques from the machine-learning domain to solve the fault diagnosis problem in the realm of electronic system design and manufacturing;• Demonstrates techniques based on industrial data and feedback from an actual manufacturing line;• Discusses practical problems, including diagnosis accuracy, diagnosis time cost, evaluation of diagnosis system, handling of missing syndromes in diagnosis, and need for fast diagnosis-system development.

This book describes a comprehensive framework for hardware/software co-design, optimization, and use of robust, low-cost, and cyberphysical digital microfluidic systems. Readers with a background in electronic design automation will find this book to be a valuable reference for leveraging conventional VLSI CAD techniques for emerging technologies, e.g., biochips or bioMEMS. Readers from the circuit/system design community will benefit from methods presented to extend design and testing techniques from microelectronics to mixed-technology microsystems. For readers from the microfluidics domain, this book presents a new design and development strategy for cyberphysical microfluidics-based biochips suitable for large-scale bioassay applications.• Takes a transformative, “cyberphysical” approach towards achieving closed-loop and sensor feedback-driven biochip operation under program control;• Presents a “physically-aware” system reconfiguration technique that uses sensor data at intermediate checkpoints to dynamically reconfigure biochips;• Enables readers to simplify the structure of biochips, while facilitating the “general-purpose” use of digital microfluidic biochips for a wider range of applications.

This book provides a comprehensive set of optimization and prediction techniques for an enterprise information system. Readers with a background in operations research, system engineering, statistics, or data analytics can use this book as a reference to derive insight from data and use this knowledge as guidance for production management. The authors identify the key challenges in enterprise information management and present results that have emerged from leading-edge research in this domain. Coverage includes topics ranging from task scheduling and resource allocation, to workflow optimization, process time and status prediction, order admission policies optimization, and enterprise service-level performance analysis and prediction. With its emphasis on the above topics, this book provides an in-depth look at enterprise information management solutions that are needed for greater automation and reconfigurability-based fault tolerance, as well as to obtain data-driven recommendations for effective decision-making.

This book describes a comprehensive framework for hardware/software co-design, optimization, and use of robust, low-cost, and cyberphysical digital microfluidic systems. Readers with a background in electronic design automation will find this book to be a valuable reference for leveraging conventional VLSI CAD techniques for emerging technologies, e.g., biochips or bioMEMS. Readers from the circuit/system design community will benefit from methods presented to extend design and testing techniques from microelectronics to mixed-technology microsystems. For readers from the microfluidics domain, this book presents a new design and development strategy for cyberphysical microfluidics-based biochips suitable for large-scale bioassay applications.• Takes a transformative, “cyberphysical” approach towards achieving closed-loop and sensor feedback-driven biochip operation under program control;• Presents a “physically-aware” system reconfiguration technique that uses sensor data at intermediate checkpoints to dynamically reconfigure biochips;• Enables readers to simplify the structure of biochips, while facilitating the “general-purpose” use of digital microfluidic biochips for a wider range of applications.

Recent microfluidic technologies have brought a complete paradigm shift in automating biochemical processing on a tiny lab-on-chip (a.k.a. biochip) that replaces expensive and bulky instruments traditionally used in implementing bench-top laboratory protocols. Biochips have already made a profound impact on various application domains such as clinical diagnostics, DNA analysis, genetic engineering, and drug discovery, among others. They are capable of precisely manipulating micro-/pico-liter quantities of fluids, and provide integrated support for mixing, storage, transportation, and sensing, on-chip. In almost all bioprotocols, sample preparation plays an important role, which includes dilution and mixing of several fluids satisfying certain volumetric ratios. However, designing algorithms that minimize reactant-cost and sample-preparation time suited for microfluidic chips poses a great challenge from the perspective of protocol mapping, scheduling, and physical design. Algorithms for Sample Preparation with Microfluidic Lab-on-Chip attempts to bridge the widening gap between biologists and engineers by introducing, from the fundamentals, several state-of-the-art computer-aided-design (CAD) algorithms for sample preparation with digital and flow-based microfluidic biochips. Technical topics discussed in the book include: Basics of digital and flow-based microfluidic lab-on-chipComprehensive review of state-of-the-art sample preparation algorithmsSample-preparation algorithms for digital microfluidic lab-on-chipSample-preparation algorithms for flow-based microfluidic lab-on-chip

A microfluidic biochip is an engineered fluidic device that controls the flow of analytes, thereby enabling a variety of useful applications. According to recent studies, the fields that are best set to benefit from the microfluidics technology, also known as lab-on-chip technology, include forensic identification, clinical chemistry, point-of-care (PoC) diagnostics, and drug discovery. The growth in such fields has significantly amplified the impact of microfluidics technology, whose market value is forecast to grow from $4 billion in 2017 to $13.2 billion by 2023. The rapid evolution of lab-on-chip technologies opens up opportunities for new biological or chemical science areas that can be directly facilitated by sensor-based microfluidics control. For example, the digital microfluidics-based ePlex system from GenMarkDx enables automated disease diagnosis and can bring syndromic testing near patients everywhere.However, as the applications of molecular biology grow, the adoption of microfluidics in many applications has not grown at the same pace, despite the concerted effort of microfluidic systems engineers. Recent studies suggest that state-of-the-art design techniques for microfluidics have two major drawbacks that need to be addressed appropriately: (1) current lab-on-chip systems were only optimized as auxiliary components and are only suitable for sample-limited analyses; therefore, their capabilities may not cope with the requirements of contemporary molecular biology applications; (2) the integrity of these automated lab-on-chip systems and their biochemical operations are still an open question since no protection schemes were developed against adversarial contamination or result-manipulation attacks. Optimization of Trustworthy Biomolecular Quantitative Analysis Using Cyber-Physical Microfluidic Platforms provides solutions to these challenges by introducing a new design flow based on the realistic modeling of contemporary molecular biology protocols. It also presents a microfluidic security flow that provides a high-level of confidence in the integrity of such protocols. In summary, this book creates a new research field as it bridges the technical skills gap between microfluidic systems and molecular biology protocols but it is viewed from the perspective of an electronic/systems engineer.

The rapid growth in big data from mobile, Internet of things (IoT), and edge devices, and the continued demand for higher computing power, have established deep learning as the cornerstone of most artificial intelligence (AI) applications today. Recent years have seen a push towards deep learning implemented on domain-specific AI accelerators that support custom memory hierarchies, variable precision, and optimized matrix multiplication. Commercial AI accelerators have shown superior energy and footprint efficiency compared to GPUs for a variety of inference tasks.In this monograph, roadblocks that need to be understood and analyzed to ensure functional robustness in emerging AI accelerators are discussed. State-of-the-art practices adopted for structural and functional testing of the accelerators are presented, as well as methodologies for assessing the functional criticality of hardware faults in AI accelerators for reducing the test time by targeting the functionally critical faults.This monograph highlights recent research on efforts to improve test and reliability of neuromorphic computing systems built using non-volatile memory (NVM) devices like spin-transfer-torque (STT-MRAM) and resistive RAM (ReRAM) devices. Also are the robustness of silicon-photonic neural networks and the reliability concerns with manufacturing defects and process variations in monolithic 3D (M3D) based near-memory computing systems.

This book provides readers with a valuable guide to understanding security and the interplay of computer science, microfluidics, and biochemistry in a biochip cyberphysical system (CPS). The authors uncover new, potential threat and trust-issues to address, as this emerging technology is poised to be adapted at a large scale. Readers will learn how to secure biochip CPS by leveraging the available resources in different application contexts, as well as how to ensure intellectual property (IP) is protected against theft and counterfeits. This book enables secure biochip CPS design by helping bridge the knowledge gap at the intersection of the multi-disciplinary technology that drives biochip CPS.

This book provides readers with a valuable guide to understanding security and the interplay of computer science, microfluidics, and biochemistry in a biochip cyberphysical system (CPS). The authors uncover new, potential threat and trust-issues to address, as this emerging technology is poised to be adapted at a large scale. Readers will learn how to secure biochip CPS by leveraging the available resources in different application contexts, as well as how to ensure intellectual property (IP) is protected against theft and counterfeits. This book enables secure biochip CPS design by helping bridge the knowledge gap at the intersection of the multi-disciplinary technology that drives biochip CPS.

This book tackles important problems of anomaly detection and health status analysis in complex core router systems, integral to today’s Internet Protocol (IP) networks. The techniques described provide the first comprehensive set of data-driven resiliency solutions for core router systems. The authors present an anomaly detector for core router systems using correlation-based time series analysis, which monitors a set of features of a complex core router system. They also describe the design of a changepoint-based anomaly detector such that anomaly detection can be adaptive to changes in the statistical features of data streams. The presentation also includes a symbol-based health status analyzer that first encodes, as a symbol sequence, the long-term complex time series collected from a number of core routers, and then utilizes the symbol sequence for health analysis. Finally, the authors describe an iterative, self-learning procedure for assessing the health status.Enables Accurate Anomaly Detection Using Correlation-Based Time-Series Analysis;Presents the design of a changepoint-based anomaly detector;Includes Hierarchical Symbol-based Health-Status Analysis;Describes an iterative, self-learning procedure for assessing the health status.

This book describes novel hardware security and microfluidic biochip design methodologies to protect against tampering attacks in cyberphysical microfluidic biochips (CPMBs). It also provides a general overview of this nascent area of research, which will prove to be a vital resource for practitioners in the field.This book shows how hardware-based countermeasures and design innovations can be a simple and effective last line of defense, demonstrating that it is no longer justifiable to ignore security and trust in the design phase of biochips.

A microfluidic biochip is an engineered fluidic device that controls the flow of analytes, thereby enabling a variety of useful applications. According to recent studies, the fields that are best set to benefit from the microfluidics technology, also known as lab-on-chip technology, include forensic identification, clinical chemistry, point-of-care (PoC) diagnostics, and drug discovery. The growth in such fields has significantly amplified the impact of microfluidics technology, whose market value is forecast to grow from $4 billion in 2017 to $13.2 billion by 2023. The rapid evolution of lab-on-chip technologies opens up opportunities for new biological or chemical science areas that can be directly facilitated by sensor-based microfluidics control. For example, the digital microfluidics-based ePlex system from GenMarkDx enables automated disease diagnosis and can bring syndromic testing near patients everywhere.However, as the applications of molecular biology grow, the adoption of microfluidics in many applications has not grown at the same pace, despite the concerted effort of microfluidic systems engineers. Recent studies suggest that state-of-the-art design techniques for microfluidics have two major drawbacks that need to be addressed appropriately: (1) current lab-on-chip systems were only optimized as auxiliary components and are only suitable for sample-limited analyses; therefore, their capabilities may not cope with the requirements of contemporary molecular biology applications; (2) the integrity of these automated lab-on-chip systems and their biochemical operations are still an open question since no protection schemes were developed against adversarial contamination or result-manipulation attacks. Optimization of Trustworthy Biomolecular Quantitative Analysis Using Cyber-Physical Microfluidic Platforms provides solutions to these challenges by introducing a new design flow based on the realistic modeling of contemporary molecular biology protocols. It also presents a microfluidic security flow that provides a high-level of confidence in the integrity of such protocols. In summary, this book creates a new research field as it bridges the technical skills gap between microfluidic systems and molecular biology protocols but it is viewed from the perspective of an electronic/systems engineer.

This book tackles important problems of anomaly detection and health status analysis in complex core router systems, integral to today’s Internet Protocol (IP) networks. The techniques described provide the first comprehensive set of data-driven resiliency solutions for core router systems. The authors present an anomaly detector for core router systems using correlation-based time series analysis, which monitors a set of features of a complex core router system. They also describe the design of a changepoint-based anomaly detector such that anomaly detection can be adaptive to changes in the statistical features of data streams. The presentation also includes a symbol-based health status analyzer that first encodes, as a symbol sequence, the long-term complex time series collected from a number of core routers, and then utilizes the symbol sequence for health analysis. Finally, the authors describe an iterative, self-learning procedure for assessing the health status.Enables Accurate Anomaly Detection Using Correlation-Based Time-Series Analysis;Presents the design of a changepoint-based anomaly detector;Includes Hierarchical Symbol-based Health-Status Analysis;Describes an iterative, self-learning procedure for assessing the health status.

This book describes novel hardware security and microfluidic biochip design methodologies to protect against tampering attacks in cyberphysical microfluidic biochips (CPMBs). It also provides a general overview of this nascent area of research, which will prove to be a vital resource for practitioners in the field.This book shows how hardware-based countermeasures and design innovations can be a simple and effective last line of defense, demonstrating that it is no longer justifiable to ignore security and trust in the design phase of biochips.

Recent microfluidic technologies have brought a complete paradigm shift in automating biochemical processing on a tiny lab-on-chip (a.k.a. biochip) that replaces expensive and bulky instruments traditionally used in implementing bench-top laboratory protocols. Biochips have already made a profound impact on various application domains such as clinical diagnostics, DNA analysis, genetic engineering, and drug discovery, among others. They are capable of precisely manipulating micro-/pico-liter quantities of fluids, and provide integrated support for mixing, storage, transportation, and sensing, on-chip. In almost all bioprotocols, sample preparation plays an important role, which includes dilution and mixing of several fluids satisfying certain volumetric ratios. However, designing algorithms that minimize reactant-cost and sample-preparation time suited for microfluidic chips poses a great challenge from the perspective of protocol mapping, scheduling, and physical design. Algorithms for Sample Preparation with Microfluidic Lab-on-Chip attempts to bridge the widening gap between biologists and engineers by introducing, from the fundamentals, several state-of-the-art computer-aided-design (CAD) algorithms for sample preparation with digital and flow-based microfluidic biochips. Technical topics discussed in the book include: Basics of digital and flow-based microfluidic lab-on-chipComprehensive review of state-of-the-art sample preparation algorithmsSample-preparation algorithms for digital microfluidic lab-on-chipSample-preparation algorithms for flow-based microfluidic lab-on-chip

This book provides readers with an insightful guide to the design, testing and optimization of 2.5D integrated circuits. The authors describe a set of design-for-test methods to address various challenges posed by the new generation of 2.5D ICs, including pre-bond testing of the silicon interposer, at-speed interconnect testing, built-in self-test architecture, extest scheduling, and a programmable method for low-power scan shift in SoC dies. This book covers many testing techniques that have already been used in mainstream semiconductor companies. Readers will benefit from an in-depth look at test-technology solutions that are needed to make 2.5D ICs a reality and commercially viable.

This book provides a comprehensive overview of flow-based, microfluidic VLSI. The authors describe and solve in a comprehensive and holistic manner practical challenges such as control synthesis, wash optimization, design for testability, and diagnosis of modern flow-based microfluidic biochips. They introduce practical solutions, based on rigorous optimization and formal models. The technical contributions presented in this book will not only shorten the product development cycle, but also accelerate the adoption and further development of modern flow-based microfluidic biochips, by facilitating the full exploitation of design complexities that are possible with current fabrication techniques.