Article
Engineering, Electrical & Electronic
Samith Hettiarachchi, Gehan Melroy, Amith Mudugamuwa, Peshan Sampath, Charith Premachandra, Ranjith Amarasinghe, Van Dau
Summary: This paper discusses the modeling, simulation, and experimentation of an active droplet generator used in LOC devices, focusing on the key factor of droplet generation. The study found that optimized droplet contraction width and flow rate ratios are crucial for effective droplet generation. Experimental results show that droplet formation occurs within a certain range of flow rate ratios, with droplet diameter decreasing as the flow rate ratio decreases.
SENSORS AND ACTUATORS A-PHYSICAL
(2021)
Article
Automation & Control Systems
Chen Jiang, Rong-Quan Yang, Bo Yuan
Summary: This paper proposes an evolutionary algorithm-based droplet routing method to minimize the arrival time of droplets. The method uses priority encoding and an improved decoding strategy to flexibly arrange the droplets and modifies the paths to satisfy fluidic constraints.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2022)
Review
Biochemical Research Methods
Si Kuan Thio, Sung-Yong Park
Summary: Electrowetting-on-dielectric (EWOD) is an active-type technology for small-scale liquid handling that offers unique advantages such as no requirement of mechanical components, low power consumption, and rapid response time. However, conventional EWOD devices often require complex fabrication processes and face integration challenges. Optoelectrowetting (OEW), a light-driven mechanism, has emerged as an alternative approach that allows for dynamic control of electrowetting without complex control circuitry. OEW has been explored for potential applications in biology, biochemistry, and as portable smartphone-integrated environmental sensors.
Review
Chemistry, Analytical
Zhenqi Jiang, Haoran Shi, Xiaoying Tang, Jieling Qin
Summary: Single-cell manipulation and analysis is crucial for studying biological processes and cellular heterogeneity. Droplet microfluidics has revolutionized single-cell studies by allowing extraction of information at the genomic, transcriptomic, proteomic, and metabolomic level from individual cells. It provides a detailed understanding of intracellular heterogeneity and changes in cellular phenotype. This review summarizes the advances of droplet microfluidics in single-cell analysis, emphasizing its advantages, preparation, and various assays. It also highlights its application in studying gene expression and other biomolecules at the single-cell level, facilitating fundamental and clinical research.
TRAC-TRENDS IN ANALYTICAL CHEMISTRY
(2023)
Review
Biotechnology & Applied Microbiology
Hao Sun, Wantao Xie, Jin Mo, Yi Huang, Hui Dong
Summary: Droplet microfluidics has become popular due to its advantages in high throughput, high integration, high sensitivity, and low power consumption. With the development of computer technology, deep learning has been able to process large amounts of data, contributing greatly to various fields. Intelligent microfluidics has emerged as a result, with the potential for developing automated and intelligent devices by integrating microfluidic technology with artificial intelligence. This article provides a general review of the evolution of intelligent microfluidics and its applications in droplet generation, control, and analysis, as well as discussing the challenges and opportunities in this field.
FRONTIERS IN BIOENGINEERING AND BIOTECHNOLOGY
(2023)
Article
Chemistry, Analytical
Chiharu Shiro, Hiroki Nishikawa, Xiangbo Kong, Hiroyuki Tomiyama, Shigeru Yamashita
Summary: This paper proposes a routing method for droplets on a micro-electrode dot array (MEDA) biochip, which can significantly reduce the biochip size and enable parallel manipulation of droplets.
Review
Chemistry, Multidisciplinary
Samaneh Zare Harofte, Madjid Soltani, Saeed Siavashy, Kaamran Raahemifar
Summary: Nowadays, artificial intelligence creates numerous opportunities in the life sciences. Integrating AI with microfluidics can significantly impact various fields of biotechnology.
Article
Materials Science, Multidisciplinary
Kartik Totlani, Yen-Chieh Wang, Maxime Bisschops, Thorben de Riese, Michiel T. Kreutzer, Walter M. van Gulik, Volkert van Steijn
Summary: This study addresses a key bottleneck in bioprocess development by developing a droplet-based fed-batch nanobioreactor. The ability to study micro-organisms under nutrient-controlled fed-batch conditions is demonstrated, offering a solid platform technology for further development and use in the field of bioprocess development and beyond.
ADVANCED MATERIALS TECHNOLOGIES
(2021)
Article
Management
Ramin Raeesi, Konstantinos G. Zografos
Summary: This paper proposes a multi-faceted approach to increase the driving range of Electric Commercial Vehicles (ECVs) through intra-route recharging and synchronised en-route battery swapping services. The proposed logistics model shows significant cost and emissions savings.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2022)
Article
Computer Science, Interdisciplinary Applications
Ying Liu, Jing Zhou, Andrew Lim, Qian Hu
Summary: This study focuses on the unit-capacity resource constrained project scheduling problem with transfer times, aiming to minimize the project makespan by transforming it into a multiple traveling salesperson problem. Two heuristics are proposed to solve the problem, with computational experiments showing the good performance of the approaches in generating feasible schedules.
COMPUTERS & INDUSTRIAL ENGINEERING
(2021)
Article
Chemistry, Physical
Cristina Chircov, Alexandra Catalina Birca, Alexandru Mihai Grumezescu, Bogdan Stefan Vasile, Ovidiu Oprea, Adrian Ionut Nicoara, Chih-Hui Yang, Keng-Shiang Huang, Ecaterina Andronescu
Summary: The study introduced a new method for the synthesis of magnetite nanoparticles using a microfluidic lab-on-chip device to achieve controlled properties. It investigated the influence of iron precursor solution concentration and flow on the final properties of the nanomaterials, ultimately achieving uniformity and stability in the synthesized nanoparticles.
Article
Computer Science, Information Systems
Kaiser Parnamets, Andres Udal, Ants Koel, Tamas Pardy, Nafisat Gyimah, Toomas Rang
Summary: This study presents the construction of a compact empirical mathematical model for predicting the generation speed of droplets in a flow-focusing microfluidic system. By interpreting experimental results, a high-accuracy model suitable for typical applications was obtained. The novelties of this study include the use of a linear approximation model to describe droplet diameter suppression, the introduction of a new shape parameter, and the proposal of a machine learning correction function.
Article
Engineering, Chemical
Julia Sophie Boeke, Daniel Kraus, Thomas Henkel
Summary: Reliable operation of lab-on-a-chip systems relies on precise and predictable fluid management, with pressure-driven flow control simplifying the process and enabling automation. Predicting the required pressure settings for achieving desired flow rates simplifies control tasks and offers opportunities for creating multi-component laminar flows and droplets.
Review
Chemistry, Analytical
Ruth Shinar, Joseph Shinar
Summary: Organic electronics (OE) technology has matured in displays and is advancing in solid-state lighting applications. It also shows great potential in (bio)chemical sensing, imaging, in vitro cell monitoring, and other biomedical diagnostics. OE devices, such as organic LEDs, organic and hybrid perovskite-based photodetectors, and organic thin-film transistors, are utilized in these applications. The integration of compact and sensitive OE devices with microfluidic channels and lab-on-a-chip (LOC) structures is very promising.
Article
Chemistry, Analytical
Daphika S. Dkhar, Rohini Kumari, Shweta J. Malode, Nagaraj P. Shetti, Pranjal Chandra
Summary: Lab-on-a-chip (LOC) biosensors have attracted research interest for their potential in personal healthcare and disease diagnostics. This review discusses various fabrication methods and techniques for designing LOC sensing devices, as well as the types of transduction systems that play a crucial role in sensing. The detection of analytes, including small molecules, macromolecules, and cells, is comprehensively reviewed with illustrations and tables.
JOURNAL OF PHARMACEUTICAL AND BIOMEDICAL ANALYSIS
(2023)
Article
Computer Science, Hardware & Architecture
Biresh Kumar Joardar, Aryan Deshwal, Janardhan Rao Doppa, Partha Pratim Pande, Krishnendu Chakrabarty
Summary: Resistive random-access memory (ReRAM)-based architectures can accelerate convolutional neural network (CNN) training, but existing architectures have limited support for normalization operations. This research proposes DeepTrain, a heterogeneous architecture enabled by Bayesian optimization, which provides the necessary hardware and software support for normalization operations and determines the minimum number of normalization operations required for each CNN. Experimental results show that the BO-enabled DeepTrain architecture achieves up to 15x speedup compared to traditional GPU training of CNNs without sacrificing accuracy.
IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS
(2022)
Article
Computer Science, Hardware & Architecture
Debraj Kundu, Sudip Roy, Sukanta Bhattacharjee, Sohini Saha, Krishnendu Chakrabarty, Partha Pratim Chakrabarti, Bhargab B. Bhattacharya
Summary: Microfluidic biochips have shown great potential and versatility in automating biochemical protocols. Sample preparation, an essential part of these protocols, involves mixing fluids at a small scale. This article explores the impact of different mixing models on the dynamics of mixing steps and proposes factorization-based and volume-oriented dilution algorithms that outperform existing algorithms in terms of reactant cost, mixing time, and waste production for micro-electrode-dot-array biochips.
IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS
(2022)
Article
Computer Science, Hardware & Architecture
Wei-Kai Liu, Benjamin Tan, Jason M. Fung, Ramesh Karri, Krishnendu Chakrabarty
Summary: To meet design requirements and application needs, designers integrate multiple IPs to produce a SoC. For improved survivability, patching the SoC is necessary to mitigate potential security issues. We propose adding programmable hardware-based support for monitoring and bug mitigation. Our approach guides designers to maximize the benefits of adding patchability to various IPs in the system, given a target resource overhead. Experimental results show superior patchability compared to other approaches, with a viable patching infrastructure generated within a specified cost limit.
IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS
(2023)
Article
Computer Science, Hardware & Architecture
Renjian Pan, Xin Li, Krishnendu Chakrabarty
Summary: To address the challenge of root-cause analysis in complex integrated systems, a multialgorithm two-stage clustering method with transfer learning is proposed in this article. The method utilizes machine learning techniques and does not rely on root-cause labels obtained from human experts. It demonstrates superior performance in two case studies based on network products, outperforming other state-of-the-art methods.
IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS
(2023)
Article
Computer Science, Hardware & Architecture
Arjun Chaudhuri, Sanmitra Banerjee, Jinwoo Kim, Sung Kyu Lim, Krishnendu Chakrabarty
Summary: The testing and diagnosis of interlayer vias (ILVs) in monolithic 3-D (M3D) ICs are crucial for increasing yield and improving product quality. A new BIST framework is proposed to address the challenges of testing and localizing faults in realistic ILV layouts, optimizing for test time and performance overhead. Evaluation results show the effectiveness of the proposed framework in M3D benchmarks.
IEEE TRANSACTIONS ON VERY LARGE SCALE INTEGRATION (VLSI) SYSTEMS
(2023)
Article
Computer Science, Hardware & Architecture
Rana Elnaggar, Jayeeta Chaudhuri, Ramesh Karri, Krishnendu Chakrabarty
Summary: Computing platforms are integrating field-programmable gate arrays (FPGAs) to support domain-specific customization, but attackers can abuse this capability by programming the FPGAs with malicious functions. This paper proposes a defense based on machine learning algorithms to detect bitstreams of malicious circuits and malicious circuits mixed with legitimate circuits by analyzing static features extracted from FPGA bitstreams. The results show that this approach can identify malicious circuits with a false-positive rate of only 4% and a true-positive rate of 100% without the need for reverse engineering.
IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS
(2023)
Article
Computer Science, Hardware & Architecture
Mahmoud Elfar, Yi-Chen Chang, Harrison Hao-Yu Ku, Tung-Che Liang, Krishnendu Chakrabarty, Miroslav Pajic
Summary: The study introduces a deep reinforcement learning (DRL)-based approach to bypass degraded electrodes and enhance the reliability of routing. Simulation results show that the proposed approach provides effective routing strategies for COVID-19 testing protocols. Experimental results validate the feasibility of the DRL-based approach and demonstrate its superiority in terms of reduced clock cycles and shorter execution time compared to baseline methods.
IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS
(2023)
Article
Computer Science, Hardware & Architecture
Biresh Kumar Joardar, Tyler K. Bletsch, Krishnendu Chakrabarty
Summary: Rowhammer is a security vulnerability caused by the electrical interaction between adjacent rows in DRAMs. We propose a machine learning-based solution that can detect and prevent Rowhammer attacks reliably. This method has lower power and area overhead compared to existing solutions.
IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS
(2023)
Article
Engineering, Electrical & Electronic
Sanmitra Banerjee, Mahdi Nikdast, Krishnendu Chakrabarty
Summary: Integrated photonic neural networks (IPNNs) are being considered as promising alternatives to traditional electronic AI accelerators due to their improved computing speed and energy efficiency. However, the accuracy of IPNNs can be negatively affected by imperfections in the underlying MZI devices, including variations in lithography and thermal crosstalk. In this article, we systematically analyze the impact of such imperfections on IPNN accuracy and identify critical components that can lead to significant degradation. Our findings highlight the importance of addressing these imperfections to improve the reliability of IPNNs.
JOURNAL OF LIGHTWAVE TECHNOLOGY
(2023)
Article
Computer Science, Hardware & Architecture
Sanmitra Banerjee, Mahdi Nikdast, Krishnendu Chakrabarty
Summary: This article presents a method of criticality assessment to identify susceptible components of silicon-photonic neural networks.
IEEE DESIGN & TEST
(2023)
Article
Computer Science, Information Systems
Farshad Firouzi, Shiyi Jiang, Krishnendu Chakrabarty, Bahar Farahani, Mahmoud Daneshmand, Jaeseung Song, Kunal Mankodiya
Summary: The digital transformation involves the convergence of technologies such as IoT, edge-fog-cloud computing, AI, and blockchain, blurring the lines between the physical and digital worlds. While these innovations have developed independently, they are increasingly intertwined, driving new business models. However, the adoption of this convergence is still in its early stages, facing issues such as a lack of consensus and best practices. This article provides a comprehensive insight into the fusion of these technologies, discussing requirements, reference architectures, applications, and challenges, and presenting a case study on privacy-preserving stress monitoring and management.
IEEE INTERNET OF THINGS JOURNAL
(2023)
Article
Computer Science, Information Systems
Jin Zhou, Jackson McNabb, Nick DeCapite, Jose R. Ruiz, Deborah A. Fisher, Sonia Grego, Krishnendu Chakrabarty
Summary: This article presents a stool image analysis approach for classifying the form and color of stool using an IoT-based smart toilet. The researchers constructed a dataset of 3275 stool images, annotated by two gastroenterologists, and used convolutional neural networks and machine-learning techniques to achieve accurate classification of stool form and color. They also utilized an edge-cloud approach to optimize the balance between accuracy and latency in the classification process.
IEEE INTERNET OF THINGS JOURNAL
(2023)
Article
Computer Science, Information Systems
Biresh Kumar Joardar, Janardhan Rao Doppa, Hai Li, Krishnendu Chakrabarty, Partha Pratim Pande
Summary: Training ML models at the edge can solve privacy/security issues, improve accessibility, and meet real-time requirements. However, existing edge platforms lack computing power for complex tasks. ReRAM-based architectures offer high-performance computing for on-chip CNN training, but lack scalability. This paper proposes a crossbar-aware pruning strategy, ReaLPrune, which can prune over 90% of weights, reducing hardware requirements and accelerating training.
IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTING
(2023)
Article
Computer Science, Hardware & Architecture
Ling Zhang, Zipeng Li, Xing Huang, Krishnendu Chakrabarty
Summary: Digital microfluidic biochips are a promising alternative for laboratory procedures, and this study proposes a new daisy-chain design approach that integrates a self-repair scheme to automatically detect and correct faults. It also presents an efficient test generation method to achieve 100% fault coverage.
IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS
(2023)
Article
Computer Science, Hardware & Architecture
Xiaoxuan Yang, Huanrui Yang, Janardhan Rao Doppa, Partha Pratim Pande, Krishnendu Chakrabartys, Hai Li
Summary: PIM enables energy-efficient deployment of CNNs, but the limited write endurance of ReRAM-based PIM hinders neural network training. To address this, we propose an endurance-aware framework called ESSENCE, which reduces weight reprogrammings by dynamically adjusting the probability of gradient updates. Experimental results show that ESSENCE can extend ReRAM's lifetime for training and achieve significant savings in update counts.
IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS
(2023)