Vector Symbolic Architectures (VSAs), also known as hyperdimensional (HD) computing, are increasingly deployed in cognitive applications due to their simple and efficient operations. The widespread adoption has, in turn, spurred the development of a diverse set of hardware solutions that optimize VSA performance for embedded and edge AI systems. Despite these advances, there remains a lack of comprehensive, unified discussion on the co-design and co-evolution of VSA algorithms and hardware. This survey aims at bridging that gap by linking theoretical, software-level explorations with efficient hardware architectures and emerging technology fabrics for VSAs, providing co-design insights that are accessible to both algorithm and hardware communities. First, we introduce the principles of vector-symbolic computing, including its core mathematical operations and learning paradigms. Second, we provide an in-depth discussion on hardware technologies for VSAs, analyzing analog, mixed-signal, and digital circuit design styles. We compare hardware implementations of VSAs by carrying out detailed analysis of their performance characteristics and tradeoffs, from which we distill design guidelines that are applicable across arbitrary VSA formulations. Third, we discuss a methodology for cross-layer design of VSAs that identifies synergies across layers and explores key ingredients for hardware/software co-design of VSAs. Finally, as a concrete case study of this methodology, we present an in-memory computing hardware design for VSA-based hierarchical cognition, illustrating how the proposed co-design principles translate into efficient architectures. The article concludes with a discussion of open research challenges and opportunities for future explorations.

Large language models (LLMs) have significantly transformed the landscape of artificial intelligence, demonstrating exceptional capabilities in natural language understanding and generation. Recently, the integration of LLMs with neurosymbolic architectures has gained traction to enhance contextual awareness and planning capabilities. However, this integration faces computational challenges that hinder scalability and efficiency, especially in edge computing environments. This paper provides an in-depth analysis of these challenges and explores state-of-the-art solutions, focusing on memory-centric computing principles at both algorithmic and hardware levels. Our exploration is centered around the key computational elements of the Transformer, the foundation of all LLMs, and vector-symbolic architecture, the leading neuro-symbolic model for edge applications. Additionally, we propose potential research directions for further investigation. By examining these aspects, this paper aims to bridge critical gaps in the path toward effective artificial general intelligence at the edge.

The methodology used to design and optimize the very first general-purpose hyperdimensional (HD) processing unit capable of executing a broad spectrum of HD workloads (called “HPU”) is presented. HD computing is a brain-inspired computational paradigm that uses the principles of high-dimensional mathematics to perform cognitive tasks. While considerable efforts have been spent toward realizing efficient HD processors, all of these targeted specific application domains, most often pattern classification. In contrast, the HPU design addresses the multiple layers of a cognitive process. A structured methodology identifies the kernel HD computations recurring at each of these layers, and maps them onto a unified and parameterized architectural model. The effectiveness in terms of runtime and energy consumption of the approach is evaluated. The results show that the resulting HPU efficiently processes the full range of HD algorithms, and far outperforms baseline implementations on a GPU.

Disentangling attributes of various sensory signals is central to human-like perception and reasoning and a critical task for higher-order cognitive and neuro-symbolic AI systems. An elegant approach to represent this intricate factorization is via high-dimensional holographic vectors drawing on brain-inspired vector symbolic architectures. However, holographic factorization involves iterative computation with high-dimensional matrix-vector multiplications and suffers from non-convergence problems. In this paper, we present H3DFact, a heterogeneous 3D integrated in-memory compute engine capable of efficiently factorizing high-dimensional holographic representations. H3DFact exploits the computation-in-superposition capability of holographic vectors and the intrinsic stochasticity associated with memristive-based 3D compute-in-memory. Evaluated on large-scale factorization and perceptual problems, H3DFact demonstrates superior capability in factorization accuracy and operational capacity by up to five orders of magnitude, with 5.5 x compute density, 1.2 x energy efficiency improvements, and 5.9 x less silicon footprint compared to iso-capacity 2D designs.

Cooperative embodied AI systems, where multiple agents collaborate to accomplish complex, long-horizon tasks, show significant promise for real-world applications. These systems integrate perception, cognition, and action through integrated task and motion planning (TAMP), leveraging the advanced reasoning and communication capabilities of large language models (LLMs). However, their efficiency is often hindered by challenges such as high computational latency and redundant communication, largely due to the reliance on LLMs for sequential planning decisions.

Microfluidic platforms have recently emerged as an invaluable component for studying synthetic biology as they are capable of emulating complex molecular networks of biological pathways (biocircuits) on a chip. A special type of biochemical assays, known as biocircuit-regulatory scanning (BRS) assays, is employed to regulate gene expression, enabling comprehensive exploration of related biocircuit parameters. Prior work has provided high-level design methodologies for implementing BRS; however, most of these methods are abstract and cannot be used in practice as they overlook the dynamics of interactions between the samples and the biochip. In this article, we address this limitation by providing a comprehensive framework that implements BRS assays. The proposed framework, named BioScan, includes: 1) a statistical method that selects suitable volumetric ratios of biochemicals used to execute a BRS assay; 2) a high-level synthesis method that generates the specifications of the target BRS assay; 3) a translation technique enabling implementation of BRS on a microelectrode-dot array (MEDA) biochip; and 4) a Dirichlet-regressor that constructs the parameter space of the associated biocircuit. Simulation results show that the proposed framework can efficiently perform parameter-space exploration (PSE) while significantly reducing completion time and reagent cost.

This paper presents a research vision to design a large-scale cyberphysical systems (CPS) experimental framework to enable collaborative and coordinated molecular biology studies. This framework will be based on the integration of CPS with microfluidic biochips and cloud computing. It has the potential to drastically advance personalized medicine through knowledge fusion among many research groups, and synchronization of research planning. This framework therefore leads to a better understanding of diseases such as cancer, and helps researchers in identifying effective treatments. A case study from cancer research is discussed to explain the significance of our framework in promoting coordinated genomic studies.

Single-cell analysis is used to gain insights into diseases, such as cancer. Advances in microfluidic solutions have enabled the efficient classification and analysis of a heterogeneous population of cells. Recently, a hybrid microfluidic platform was proposed for concurrent single-cell analysis on thousands of heterogeneous cells. In this design, barcoding droplets are routed using a valve-based routing fabric to label the input cells. However, prior work overlooked defects that are likely to occur during chip fabrication and system integration and the fault tolerance of this routing fabric remains a major concern. We address the above limitation and introduce a low-overhead design technique for guaranteeing the tolerance of single faults, while maintaining the efficiency of the cell-analysis platform. We show that the proposed method is optimal in that it minimizes the overhead in terms of fabric size.

Digital microfluidic biochips (DMFBs) are an emerging technology that implements bioassays through manipulation of discrete fluid droplets. Recent results have shown that DMFBs are vulnerable to actuation tampering attacks, where a malicious adversary modifies control signals for the purposes of manipulating results or causing denial-of-service. Such attacks leverage the highly programmable nature of DMFBs. However, practical DMFBs often employ a technique called pin mapping to reduce control pin count while simultaneously reducing the degrees of freedom available for droplet manipulation. Attempts to control specific electrodes as part of an attack cannot be made without inadvertently actuating other electrodes on-chip, which makes the tampering evident. This paper explores this tamper resistance property of pin mapping in detail. We derive relevant security metrics, evaluate the tamper resistance of several existing pin mapping algorithms, and propose a new security-aware pin mapper. Further, we develop integer linear programming-based methodologies for inserting indicator droplets into a DMFB in order to boost tamper resistance. Experimental results show that the proposed techniques can significantly increase the difficulty for an attacker to make stealthy changes to the execution of a bioassay.

Microfluidic routing fabrics are reconfigurable primitives that permit the dynamic redirection of fluids on a flow-based microfluidic biochip. Such primitives are bringing the benefits of rapid prototyping and on-the-fly reconfigurability from integrated circuits to the microfluidic domain. An unfortunate side effect of this increased flexibility is susceptibility to tampering. A malicious adversary can alter either the electronic control signals or the pneumatic control lines used to drive the routing fabric. In this paper, we provide a high-level security assessment of microfluidic systems utilizing routing fabrics, and analyze their security under actuation tampering attacks. We show that under reasonable assumptions, the permissible states of a routing fabric form a probability distribution. We provide methods for efficiently determining this distribution through a binary tree representation. We then show how to synthesize routings fabrics that exhibit well-defined behaviors. We call a routing fabric designed in such a way tamper-mitigating, as it makes the effects of tampering probabilistically less severe. We then show how the proposed methodology can be used to protect a forensic DNA barcoding application from attack.

DNA fingerprinting can offer remarkable benefits, especially for point-of-care diagnostics, information forensics, and analysis. However, the pressure to drive down costs is likely to lead to cheap untrusted solutions and a multitude of unprecedented risks. These risks will especially emerge at the frontier between the cyberspace and DNA biology. To address these risks, we perform a forensic-security assessment of a typical DNA-fingerprinting flow. We demonstrate, for the first time, benchtop analysis of biochemical-level vulnerabilities in flows that are based on a standard quantification assay known as polymerase chain reaction (PCR). After identifying potential vulnerabilities, we realize attacks using benchtop techniques to demonstrate their catastrophic impact on the outcome of the DNA fingerprinting. We also propose a countermeasure, in which DNA samples are each uniquely barcoded (using synthesized DNA molecules) in advance of PCR analysis, thus demonstrating the feasibility of our approach using benchtop techniques. We discuss how molecular barcoding could be utilized within a cyber-biological framework to improve DNA-fingerprinting security against a wide range of threats, including sample forgery. We also present a security analysis of the DNA barcoding mechanism from a molecular biology perspective.

It is expected that as digital microfluidic biochips (DMFBs) mature, the hardware design flow will begin to resemble the current practice in the semiconductor industry: design teams send chip layouts to third-party foundries for fabrication. These foundries are untrusted and threaten to steal valuable intellectual property (IP). In a DMFB, the IP consists of not only hardware layouts but also of the biochemical assays (bioassays) that are intended to be executed on-chip. DMFB designers therefore must defend these protocols against theft. We propose to “lock” biochemical assays by inserting dummy mix-split operations. We experimentally evaluate the proposed locking mechanism, and show how a high level of protection can be achieved even on bioassays with low complexity. We also demonstrate a new class of attacks that exploit the side-channel information to launch sophisticated attacks on the locked bioassay.

Recent advances in microfluidic technology offer efficient platforms to emulate complex molecular networks of biological pathways (biocircuits) on a lab-on-chip. The behavior of biocircuits is governed by a number of gene-regulatory parameters. A fundamental challenge in synthesizing and verifying biocircuits is the lack of design tools that implement biocircuit-regulatory scanning (BRS) assays to explore the large parameter-space efficiently, while optimizing synthesis time and reagent cost. In this paper, we introduce an optimization flow named BioScan for systematic exploration of the parameter-space of a biocircuit. BioScan includes: (1) a statistical approach to determine a subset of mixing ratios of reagents that span the entire parameter space as densely as possible under cost constraints; (2) an ILP-based synthesis method that implements a BRS-assay on a micro-electrode dot-array biochip. Simulation results show that BioScan reduces reagent cost and enhances space-filling properties.

The integration of microfluidics and biosensor technology is transforming microbiology research by providing new capabilities for clinical diagnostics, cancer research, and pharmacology studies. This integration enables new approaches for biochemistry automation and cyber-physical adaptation. Similarly, recent years have witnessed the rapid growth of the Internet of Things (IoT) paradigm, where different types of real-world elements such as wearable sensors are connected and allowed to autonomously interact with each other. Combining the advances of both cyber-physical microfluidics and IoT domains can generate new opportunities for knowledge fusion by transforming distributed local microfluidic elements into a global network of coordinated microfluidic systems. This paper aims to streamline this transformation and it presents a research vision for enabling the Internet of Microfluidic Things (IoMT). To leverage advances in connected Microfluidic Things, we highlight new perspectives on system architecture, and describe technical challenges related to design automation, temporal flexibility, security, and service assignment. This vision is supported by case studies from cancer research and pharmacology studies to explain the significance of the proposed framework.

Single-cell genomics is used to advance our understanding of diseases, such as cancer. Microfluidic solutions have recently been developed to classify cell types or perform single-cell biochemical analysis on preisolated types of cells. However, new techniques are needed to efficiently classify cells and conduct biochemical experiments on multiple cell types concurrently. Nondeterministic cell-type identification, system integration, and design automation are major challenges in this context. To overcome these challenges, we present a hybrid microfluidic platform that enables complete single-cell analysis on a heterogeneous pool of cells. We combine this architecture with an associated design-automation and optimization framework, referred to as co-synthesis (CoSyn). The proposed framework employs real-time resource allocation to coordinate the progression of concurrent cell analysis. Besides this framework, a probabilistic model based on a discrete-time Markov chain is also deployed to investigate protocol settings, where experimental conditions, such as sonication time, vary probabilistically among cell types. Simulation results show that CoSyn efficiently utilizes platform resources and outperforms baseline techniques.

Single-cell screening is used to sort a stream of cells into clusters (or types) based on prespecified biomarkers, thus supporting type-driven biochemical analysis. Reconfigurable flow-based microfluidic biochips (RFBs) can be utilized to screen hundreds of heterogeneous cells within a few minutes, but they are overburdened with the control of a large number of valves. To address this problem, we present a pin-constrained RFB design methodology for single-cell screening. The proposed design is analyzed using computational fluid dynamics simulations, mapped to an RC-lumped model, and combined with intervalve connectivity information to construct a high-level synthesis framework, referred to as cell sorter using multiplexed control (Sortex). Simulation results show that Sortex significantly reduces the number of control pins and fulfills the timing requirements of single-cell screening.

Technological shifts in the fields of microfluidics and security are now converging. New techniques in microfluidics increasingly rely on cyberphysical integration and concepts from computer-aided design automation to provide ease-of-use, reliability, and higher throughput. Meanwhile, security concerns are extending beyond traditional information technologies as low-cost computing and sensing proliferates into an ever-increasing number of devices. This keynote paper highlights recent findings and trends in these field to motivate research in the nascent field of cyberphysical microfluidic biochip security and trust.

Flow-based microfluidic biochips can be used to perform bioassays by manipulating a large number of on-chip valves. These biochips are increasingly used today for biomolecular recognition, single-cell screening, and point-of-care disease diagnostics, and design-automation solutions for flow-based microfluidics enable the mapping and optimization of bimolecular protocols and software-based valve control. However, a key problem that has not received adequate attention is chip-to-world interfacing, which requires the use of off-chip control equipment to provide control signals for the on-chip valves. This problem is exacerbated by the increase in the number of valves as chips get more complex. To address the interfacing problem, we present an efficient pin-count minimization (synthesis) problem, referred to as Synterface, which uses on-chip microfluidic logic gates and optimization based on concepts from linear algebra. We present results to show that Synterface significantly reduces pin-count and simplifies the external interface for flow-based microfluidics.

Digital microfluidic biochips (DMFBs)---an emerging technology that implements bioassays through manipulation of discrete fluid droplets---are vulnerable to actuation tampering attacks, where a malicious adversary modifies control signals for the purposes of manipulating results or causing denial-of-service. Such attacks leverage the highly programmable nature of DMFBs. However, practical DMFBs often employ a technique called pin mapping to reduce control pin count while simultaneously reducing the degrees of freedom available for droplet manipulation. Attempts to control specific electrodes as part of an attack cannot be made without inadvertently actuating other electrodes on-chip, which makes the tampering evident. This paper explores this tamper-resistance property of pin mapping in detail. We derive relevant security metrics, evaluate the tamper-resistance of several existing pin mapping algorithms, and propose a new security-aware pin mapper with superior tamper-resistance as compared to prior work.

High-throughput single-cell genomics is used to gain insights into diseases such as cancer. Motivated by this important application, microfluidics has emerged as a key technology for developing comprehensive biochemical procedures for studying DNA, RNA, proteins, and many other cellular components. Recently, a hybrid microfluidic platform has been proposed to efficiently automate the analysis of a heterogeneous sequence of cells. In this design, a valve-based routing fabric based on transposers is used to label/barcode the target cells. However, the design proposed in prior work overlooked defects that are likely to occur during chip fabrication and system integration. We address the above limitation by investigating the fault tolerance of the valve-based routing fabric. We develop a theory of failure assessment and introduce a design technique for achieving fault tolerance. Simulation results show that the proposed method leads to a slight increase in the fabric size and decrease in cell-analysis throughput, but this is only a small price to pay for the added assurance of fault tolerance in the new design.

It is expected that as digital microfluidic biochips (DMFBs) mature, the hardware design flow will begin to resemble the current practice in the semiconductor industry: design teams send chip layouts to third party foundries for fabrication. These foundries are untrusted, and threaten to steal valuable intellectual property (IP). In a DMFB, the IP consists of not only hardware layouts, but also of the biochemical assays (bioassays) that are intended to be executed on-chip. DMFB designers therefore must defend these protocols against theft. We propose to “lock” biochemical assays through random insertion of dummy mix-split operations, subject to several design rules. We experimentally evaluate the proposed locking mechanism, and show how a high level of protection can be achieved even on bioassays with low complexity. We offer guidance on the number of dummy mixsplits required to secure a bioassay for the lifetime of a patent.

Digital microfluidics is transforming microbiology research by providing new opportunities for high-throughput sample preparation and point-of-care diagnostics. Over the past decade, several design-automation (synthesis) techniques have been developed for on-chip droplet manipulation. However, these methods oversimplify the dynamics of biomolecular protocols and they have yet to make a significant impact in biochemistry/microbiology research, leading to a large gap between advances in biochip design and the adoption of biochips for running biomolecular protocols. In this paper, we bridge this gap by introducing a new paradigm for biochip design automation. By exploiting advances in the integration of sensing systems into a digital-microfluidic biochip, we present a number of synthesis solutions that use realistic models of biomolecular protocols to address real-world microbiology applications through cyber-physical adaptation. This paper also details a vision for continued research on design-automation and optimization methodologies for the realization of biomolecular protocols using microfluidic biochips.

The interaction between technology and healthcare has a long history. However, recent years have witnessed the rapid growth and adoption of the Internet of Things (IoT) paradigm, the advent of miniature wearable biosensors, and research advances in big data techniques for effective manipulation of large, multiscale, multimodal, distributed, and heterogeneous data sets. These advances have generated new opportunities for personalized precision eHealth and mHealth services. IoT heralds a paradigm shift in the healthcare horizon by providing many advantages, including availability and accessibility, ability to personalize and tailor content, and cost-effective delivery. Although IoT eHealth has vastly expanded the possibilities to fulfill a number of existing healthcare needs, many challenges must still be addressed in order to develop consistent, suitable, safe, flexible and power-efficient systems that are suitable fit for medical needs. To enable this transformation, it is necessary for a large number of significant technological advancements in the hardware and software communities to come together. This keynote paper addresses all these important aspects of novel IoT technologies for smart healthcare-wearable sensors, body area sensors, advanced pervasive healthcare systems, and big data analytics. It identifies new perspectives and highlights compelling research issues and challenges, such as scalability, interoperability, device-network-human interfaces, and security, with various case studies. In addition, with the help of examples, we show how knowledge from CAD areas, such as large scale analysis and optimization techniques can be applied to the important problems of eHealth.

Digital microfluidic biochips (DMFBs) integrated with processors and arrays of sensors form cyberphysical systems and consequently face a variety of unique, recently described security threats. It has been noted that techniques used for error recovery can provide some assurance of integrity when a cyberphysical DMFB is under attack. This paper proposes the use of such hardware for security purposes through the randomization of checkpoints in both space and time, and provides design guidelines for designers of such systems. We define security metrics and present techniques for improving performance through static checkpoint maps, and describe performance tradeoffs associated with static and random checkpoints. We also provide detailed classification of attack models and demonstrate the feasibility of our techniques with case studies on assays implemented in typical DMFB hardware.

Flow-based microfluidic biochips are revolutionizing biochemical research by automating complex protocols and reducing sample and reagent consumption. Integration of these biochips with sensors, actuators, and intelligent control have compounded these benefits while increasing reliability. And, many flow-based platforms have successfully transitioned to the marketplace, demonstrating their utility through several recent scientific publications. However, these microfluidic technologies and platforms have unintended security and trust implications that threaten their continued success. We survey cyberphysical flow-based microfluidic platforms and perform a security assessment. We then describe an attack on digital polymerase chain reactions and how such attacks undermine research integrity.

Digital-microfluidics technology has shown considerable promise for advancing sample preparation and point-of-care diagnostics; therefore, it has the potential to transform microbiology and biochemistry research. Over the past decade, a number of microfluidics design-automation techniques have been developed for on-chip droplet manipulation. However, these methods overlook the myriad complexities of biomolecular protocols and they have yet to make a significant impact in biochemistry/microbiology research. A paradigm shift in biochip design automation and a “phase transition” in research are clearly needed to bridge this gap between microfluidics and microbiology. In this paper, we explain how researchers from design-automation and embedded systems can play a key role in this transition. We present a new synthesis flow that uses realistic models of biomolecular protocols and cyberphysical adaptation to address real-world microbiology applications. We also present a list of metrics that can be used for the assessment of design-automation techniques for microbiology applications.

Single-cell genomics is used to advance our understanding of diseases such as cancer. Microfluidic solutions have recently been developed to classify cell types or perform single-cell biochemical analysis on pre-isolated types of cells. However, new techniques are needed to efficiently classify cells and conduct biochemical experiments on multiple cell types concurrently. System integration and design automation are major challenges in this context. To overcome these challenges, we present a hybrid microfluidic platform that enables complete single-cell analysis on a heterogeneous pool of cells. We combine this architecture with an associated design-automation and optimization framework, referred to as Co-Synthesis (CoSyn). The proposed framework employs real-time resource allocation to coordinate the progression of concurrent cell analysis. Simulation results show that CoSyn efficiently utilizes platform resources and outperforms baseline techniques.

Single-cell screening is used to sort a stream of cells into clusters (or types) based on pre-specified biomarkers, thus supporting type-driven biochemical analysis. Reconfigurable flow-based microfluidic biochips (RFBs) can be utilized to screen hundreds of heterogeneous cells within a few minutes, but they are overburdened with the control of a large number of valves. To address this problem, we present a pin-constrained RFB design methodology for single-cell screening. The proposed design is analyzed using computational fluid dynamics simulations, mapped to an RC-lumped model, and combined with a high-level synthesis framework, referred to as Sortex. Simulation results show that Sortex significantly reduces the number of control pins and fulfills the timing requirements of single-cell screening.

Microfluidic routing fabrics, or crossbars, based on transposer primitives provide benefits in manufacturability, performance, and on-the-fly reconfigurability. Many applications in microfluidics, such as DNA barcoding for single-cell analysis, are expected to benefit from these new devices. However, the control of these critical devices poses new security questions that may impact the functional integrity of a microbiology application. This paper explores the many security implications of microfluidic crossbars that directly result from their structure, programmability and use in critical applications. We analyze security performance using new metrics describing how fluids can be "scattered" to incorrect locations under fault-injection attacks, and from these derive a probability model describing the likelihood of a successful attack. We present a case study of a recently described routing fabric proposed for use in a hybrid DNA barcoding platform, and discuss how fabric designers can improve security through architectural choices.

Considerable effort has recently been directed toward the implementation of molecular bioassays on digital-microfluidic biochips (DMFBs). However, today's solutions suffer from the drawback that multiple sample pathways are not supported and on-chip reconfigurable devices are not efficiently exploited. As a result, impractical manual intervention is needed to process protocols for gene-expression analysis. To overcome this problem, we first describe our benchtop experimental studies to understand gene-expression analysis and its relationship to the biochip design specification. We then introduce an integrated framework for quantitative gene-expression analysis using DMFBs. The proposed framework includes: 1) a spatial-reconfiguration technique that incorporates resource-sharing specifications into the synthesis flow; 2) an interactive firmware that collects and analyzes sensor data based on quantitative polymerase chain reaction; and 3) a real-time resource-allocation scheme that responds promptly to decisions about the protocol flow received from the firmware layer. This framework is combined with cyberphysical integration to develop the first design-automation framework for quantitative gene expression. Simulation results show that our adaptive framework efficiently utilizes on-chip resources to reduce time-to-result without sacrificing the chip's lifetime.

Advances in digital-microfluidic biochips have led to miniaturized platforms that can implement biomolecular assays. However, these designs are not adequate for running multiple sample pathways because they consider unrealistic static schedules; hence runtime adaptation based on assay outcomes is not supported and only a rigid path of bioassays can be run on the chip. We present a design framework that performs fluidic task assignment, scheduling, and dynamic decision-making for quantitative epigenetics. We first describe our benchtop experimental studies to understand the relevance of chromatin structure on the regulation of gene function and its relationship to biochip design specifications. The proposed method models biochip design in terms of real-time multiprocessor scheduling and utilizes a heuristic algorithm to solve this NP-hard problem. Simulation results show that the proposed algorithm is computationally efficient and it generates effective solutions for multiple sample pathways on a resource-limited biochip. We also present experimental results using an embedded microcontroller as a testbed.

Much progress has been made in digital microfluidic biochips (DMFB), with a great body of literature addressing low-cost, high-performance, and reliable operation. Despite this progress, security of DMFBs has not been adequately addressed. We present an analysis of a DMFB system prone to malicious modification of routes and propose a DMFB defense based on spatio-temporal randomized checkpoints using CCD cameras. Absent the knowledge of the time- and space-randomized checkpoints, an attacker cannot navigate the DMFB without alerting the system. We present an algorithm to guide the placement and timing of the checkpoints such that the probability that an attack can evade detection is minimized. The efficacy of the defense mechanism is illustrated with a case study under stealthy malicious modifications.

A digital microfluidic biochip (DMFB) is an emerging technology that enables miniaturized analysis systems for point-of-care clinical diagnostics, DNA sequencing, and environmental monitoring. A DMFB reduces the rate of sample and reagent consumption, and automates the analysis of assays. In this paper, we provide the first assessment of the security vulnerabilities of DMFBs. We identify result-manipulation attacks on a DMFB that maliciously alter the assay outcomes. Two practical result-manipulation attacks are shown on a DMFB platform performing enzymatic glucose assay on serum. In the first attack, the attacker adjusts the concentration of the glucose sample and thereby modifies the final result. In the second attack, the attacker tampers with the calibration curve of the assay operation. We then identify denial-of-service attacks, where the attacker can disrupt the assay operation by tampering either with the droplet-routing algorithm or with the actuation sequence. We demonstrate these attacks using a digital microfluidic synthesis simulator. The results show that the attacks are easy to implement and hard to detect. Therefore, this work highlights the need for effective protections against malicious modifications in DMFBs.

A digital microfluidic biochip (DMFB) is an emerging technology that enables miniaturized analysis systems for point-of-care clinical diagnostics, DNA sequencing, and environmental monitoring. A DMFB reduces the rate of sample and reagent consumption, and automates the analysis of assays. In this paper, we highlight the security vulnerabilities of DMFBs by identifying two potential attacks on a DMFB that performs enzymatic glucose assay on serum. In the first attack, the attacker adjusts the concentration of the glucose sample and thereby modifies the final result. In the second attack, the calibration curve of the assay operation is maliciously modified in order to make it deviate from the nominal/golden calibration curve. We demonstrate these attacks using a digital microluidics synthesis simulator. The results show that the attacks are stealthy as they do not result in any noticeable change in the DMFB synthesis.