This work addresses the vulnerability of trained neural network (NN) models, particularly against scan-chainbased attacks exploiting the accessibility of activation function outputs to acquire model information such as weights and biases illicitly. Our aim is to obfuscate the interconnections among the layers in the NN model controlled by secret keys stored in tamperproof memory. Our model ensures normal functionality with the correct key while disrupting data flow and producing erroneous outputs with an incorrect key. Extensive experiments demonstrate a significant drop in accuracy when incorrect keys are used, with the accuracy drop being more pronounced when the latter layers of the model are locked. The protection technique incurs 0.3% overhead in the area and 0.1% overhead in the latency of the designs.

The 2-D architecture of fully programmable valve arrays (FPVAs) is designed as a crossbar consisting of reaction chambers and microvalves, functioning as a versatile, flow-based microfluidic lab-on-chip for implementing biochemical protocols. While an FPVA enables efficient execution of various fluidic operations—such as mixing, loading, and storage, transporting fluids between chambers remains a challenging task. Furthermore, mapping a general mixing tree (representing a sequence of mixing steps) onto an FPVA is complex. It requires careful placement of reagents into specific chambers and the scheduling of subsequent mixing operations. Most sample preparation algorithms aim to generate a minimum-depth mixing tree to achieve the target mixing ratio. However, due to constraints on fluid transportation and scheduling, such a tree may not be the most practical for FPVA implementation. In this article, we harness the power of a satisfiability solver to derive a skewed mixing tree/graph that can be efficiently mapped onto an FPVA using a single mixer. This approach localizes most fluidic operations to a small region of the crossbar. Simulation results show that, for most mixing ratios, a skewed mixing tree can be found which not only reduces fluid-transportation distance and scheduling complexities but also the number of loading cycles, reagent volumes, and waste production in sample preparation, when compared to the approach based on the minimum-depth mixing tree.

Microfluidic biochips are widely used in biomedical research, clinical diagnostics, and point-of-care testing. However, their complex supply chains make them vulnerable to counterfeiting, overbuilding, and intellectual property (IP) piracy. We present fluorescent carbon quantum dot (CQD) stickers1that can be integrated with the polydimethylsiloxane (PDMS) based biochips for authentication. The stickers can be plasma-bonded to biochips made of glass and silicon. A protective spin-coated PDMS layer makes them obscured and tamperproof. However, they are detectable under UV light and can be authenticated via spectral analysis. The scheme exhibits unique excitation-dependent responses associated with the variability of the CQD sizes. This makes it ideal for physical authentication. Reliability studies concerning mechanical, photonic, and thermal degradation have demonstrated highly stable results. The stability of CQDs within the PDMS, their robust excitation-based emission fluorescence response, and the use of waste polypropylene masks make this a sustainable and robust authenticator for biochips.

Technology mapping of the logic synthesis tool ABC transforms homogeneous Boolean circuit representations (e.g., and-inverter graphs, majority-inverter graphs etc.) into Application-Specific Integrated Circuit (ASIC) targets using a cut-based Boolean matching algorithm. This entails exposing numerous k-feasible cuts to the mapping algorithm to identify an optimal match of supergate (combination of standard cells) that minimizes the overall delay without much area overhead. However, this process incurs significant timing overhead due to the need to evaluate boolean matching across an exponentially large number of cuts. We introduce LEAP: a novel machine learning-assisted cut sampling strategy that identifies and prioritizes high-quality cuts (based on delay) while filtering out low-quality ones for each node. Our extensive experimentation demonstrates that LEAP reduces the number of cuts exposed to the mapper by over 51% compared to the tool ABC, resulting in a 2% improvement in delay without incurring any area penalty. In addition, LEAP uses 35% fewer cuts with respect to the state-of-the-art SLAP tool with superior area-delay product in most cases.

In modern System-on-Chip (SoC) design, dependence on offshore fabrication has increased manyfold. This empowers SoC designers to meet stringent time-to-market demands. However, it also brings rogue entities into play, possessing security threats like IP piracy, counterfeiting and overbuilding. Several countermeasures have been proposed to thwart these threats. Among all, logic locking has been the most coveted. Logic locking is a design-for-trust technique which conceals the underlying functionality of the design to be protected by adding extra key-controlled gates. This work proposes a novel attack called NoBALL on logic locked designs using their Binary Decision Diagram (BDD) representation. We identify specific properties from the locked design’s BDD and perform intuitive intersection operations in order to identify the correct keys. Experimental analysis highlights that the current version of the attack can act as an alternative to the SAT attack and can even outperform it in some cases. Our attack can also be combined with SAT attack to form a more robust attack in future.

Flow-based microfluidic biochips (FMBs) have microvalves as key components. The physical characteristics of the microvalves vary instance-to-instance due to the inherent variability of numerous fabrication parameters. In this work, we leverage this unclonable, unpredictable instance-specific behavior and propose physically unclonable functions (PUFs) for FMBs, namely Biochip-PUFs (Bio-PUFs in short). We utilize variability in the microvalve membrane deflection response associated with the actuation pressure challenge to be our Bio-PUF parameter. Based on the distributions of the parameters measured on actual FMBs, we complement our Bio-PUF measurements via simulations of the FMB's microvalves in Comsol Multiphysics. Furthermore, we present a scheme based on the transient response of the microvalve actuation to augment the Bio-PUF authentication. The major advantage of this scheme is that we do not need any additional hardware to generate/implement the PUF module. The biochip itself can act as PUF instances while continuing to operate in normal functioning mode.

Sample preparation is an essential step in almost all bioprotocols, which can be efficiently achieved via a sequence of mixing steps called mixing graph. In the literature, several techniques have been reported to determine a mixing graph with the minimal number of mixing steps, the minimal usage of reagent fluids, the minimal wastage, or sometimes a combination of them. The retention time of a flow-based microfluidic biochip (FMB) is defined as the maximum duration for which a fluid can be stored within a microchannel without any fluid leakage. However, the retention time has not yet been considered as a scheduling constraint during the automation of the sample preparation using an FMB in order to obtain the scheduled mixing graphs. In this article, we propose a retention time-aware scheduling method called time-aware list scheduling (TALS), which can be used with the state-of-the-art methods, and a new satisfiability-based mixing algorithm called time-aware sample preparation (TASP) to obtain the scheduled mixing graph for a target ratio satisfying the retention time constraint and the number of available on-chip mixers in an FMB. Simulation results suggest that on an average TALS always outperforms a baseline scheduling method while scheduling any mixing graph, whereas TASP can determine the optimal and scheduled mixing graphs compared to the existing mixing methods combined with TALS.

Microfluidic biochips have recently emerged with significant promise and versatility in automating a variety of biochemical protocols on a tiny chip. Sample preparation, which involves the mixing of fluids with a specified target ratio in the minuscule scale, is an essential component of these protocols. Algorithms that optimize on-chip sample-preparation cost and time are closely intertwined with the underlying mixing model, mixing sequence, and fluidic architecture. Although numerous mixing models have been studied in the literature, their impact on the dynamics of mixing steps is hitherto not fully understood. In this article, we show that various mixing models can be envisaged in the light of prime factorization of integers thus establishing a connection among mixing algorithms, chip architectures, and performance. This insight has led to the development of the proposed factorization-based dilution algorithm (FacDA) considering a generalized mixing model suitable for micro-electrode-dot-array (MEDA) biochips. It further leads to target volume oriented dilution algorithm (TVODA) to cater to user’s demand for an output with a given volume. We formulate the optimization problem on the fabric of the satisfiability modulo theory (SMT) while determining mixing sequences. Simulation results on a large number of test-cases reveal thatFacDAandTVODAoutperform the state-of-the-art dilution algorithms for MEDA biochips with respect to reactant cost, mixing time, and waste production.

Microfluidic lab-on-chips offer promising technology for the automation of various biochemical laboratory protocols on a minuscule chip. Sample preparation (SP) is an essential part of any biochemical experiments, which aims to produce dilution of a sample or a mixture of multiple reagents in a certain ratio. One major objective in this area is to prepare dilutions of a given fluid with different concentration factors, each with certain volume, which is referred to as the demand-driven multiple-target (DDMT) generation problem. SP with microfluidic biochips requires proper sequencing of mix-split steps on fluid volumes and needs storage units to save intermediate fluids while producing the desired target ratio. The performance of SP depends on the underlying mixing algorithm and the availability of on-chip storage, and the latter is often limited by the constraints imposed during physical design. Since DDMT involves several target ratios, solving it under storage constraints becomes even harder. Furthermore, reduction of mix-split steps is desirable from the viewpoint of accuracy of SP, as every such step is a potential source of volumetric split error. In this article, we propose a storage-aware DDMT algorithm that reduces the number of mix-split operations on a digital microfluidic lab-on-chip. We also present the layout of the biochip with -storage cells and their allocation technique for . Simulation results reveal the superiority of the proposed method compared to the state-of-the-art multi-target SP algorithms.

A digital microfluidic biochip (DMFB) is a miniaturized laboratory capable of implementing biochemical protocols. Fully integrated DMFBs consist of a hardware platform, controller, and network connectivity, making it a cyber-physical system (CPS). A DMFB CPS is being advocated for safety-critical applications, such as medical diagnosis, drug development, and personalized medicine. Hence, the security of a DMFB CPS is of immense importance to their successful deployment. Recent research has made progress in devising corresponding defense mechanisms by employing so-called checkpoints (CPs). Existing solutions either rely on probabilistic security analysis that does not consider all possible actions an attacker may use to overcome an applied CP mechanism or rely on exhaustive monitoring of DMFB at all time-steps during the assay execution. For devising a defense scheme that is guaranteed to be secure, an exact analysis of the security of a DMFB is needed. This is not available in the current state-of-the-art. In this article, we address this issue by developing an exact method, which uses the deductive power of satisfiability solvers to verify whether a CP-based defense thwarts the execution of an attack. We demonstrate the usefulness of the proposed method by showcasing two applications on practical bioassays: 1) security analysis of various checkpointing strategies and 2) derivation of a counterexample-guided fool-proof secure CP scheme.

Researchers develop bioassays following rigorous experimentation in the lab that involves considerable fiscal and highly-skilled-person-hour investment. Previous work shows that a bioassay implementation can be reverse-engineered by using images or video and control signals of the biochip. Hence, techniques must be devised to protect the intellectual property (IP) rights of the bioassay developer. This study is the first step in this direction and it makes the following contributions: (1) it introduces the use of a dummy valve as a security primitive to obfuscate bioassay implementations; (2) it shows how dummy valves can be used to obscure biochip building blocks such as multiplexers and mixers; (3) it presents design rules and security metrics to design and measure obfuscation. In our preliminary work, we presented the concept through the use of sieve-valve as a dummy-valve. However, sieve-valves are difficult to fabricate. To overcome fabrication complexities, we propose a novel multi-height-valve as an obfuscation primitive. Moreover, we showcase the suitability of multi-height-valve for obfuscation through COMSOL simulations. We demonstrate the practicality of the proposal by fabricating an obfuscated biochip using multi-height valves. We assess the cost-security trade-offs associated with this solution and study the practical implications of dummy-valve based obfuscation on real-life biochips.

Microfluidic fully programmable valve array (FPVA) biochips have emerged as general-purpose flow-based microfluidic lab-on-chips (LoCs). An FPVA supports highly re-configurable on-chip components (modules) in the two-dimensional grid-like structure controlled by some software programs, unlike application-specific flow-based LoCs. Fluids can be loaded into or washed from a cell with the help of flows from the inlet to outlet of an FPVA, whereas cell-to-cell transportation of discrete fluid segment(s) is not precisely possible. The simplest mixing module to realize on an FPVA-based LoC is a four-way mixer consisting of a 2 × 2 array of cells working as a ring-like mixer having four valves. In this paper, we propose a design automation method for sample preparation that finds suitable placements of mixing operations of a mixing tree using four-way mixers without requiring any transportation of fluid(s) between modules. We also propose a heuristic that modifies the mixing tree to reduce the sample preparation time. We have performed an extensive simulation and examined several parameters to determine the performance of the proposed solution.

Microfluidic technologies find application in various safety-critical fields such as medical diagnostics, drug research, and cell analysis. Recent work has focused on security threats to microfluidic-based cyberphysical systems and defenses. So far the threat analysis has been limited to the cases of tampering with control software/hardware, which is common to most cyberphysical control systems in general; in a sense, such an approach is not exclusive to microfluidics. In this paper, we present a stealthy attack paradigm that uses characteristics exclusive to the microfluidic devices - a microfluidic trojan. The proposed trojan payload is a valve whose height has been perturbed to vary its pressure response. This trojan can be triggered in multiple ways based on time or specific operations. These triggers can occur naturally in a bioassay or added into the controlling software. We showcase the trojan application in carrying out practical attacks -contamination, parameter-tampering and denial-of-service - on a real-life bioassay implementation. Further, we present guidelines to launch stealthy attacks and to counter them.

Microfluidic biochips enable low-cost automation of biochemical protocols with numerous applications to medical diagnostics, forensics, molecular biology, and drug design. An important component of protocol design is sample preparation, which involves dilution or mixing of two or more fluids in a desired ratio of concentration factors (CF). Existing continuous-flow microfluidic biochips deploy either free-flowing networks where only a single layer of flow-channels is used devoid of any control valves, or valve-based technology where the flow-layer is augmented with a control layer of valves. While the former is easy to fabricate, reliable, and less expensive, they are typically hardwired for specific applications only. The latter class, although programmable, is expensive and prone to various manufacturing and operational defects. In this paper, we present the physical design of a microfluidic network that is free-flowing as well as programmable. The proposed valve-free network resembles a complete binary tree with serpentine obstacles embedded within its channels, and can be used to achieve a desired dilution of a sample just by proper selection of fluid concentrations to be fed as inputs under constant pressure. Simulation with COMSOL Multiphysics Software shows that the proposed network provides a powerful and versatile architecture for solution preparation with minimal control, outperforming prior approaches in terms of the accuracy of CFs and time for convergence.

Lab-on-chip (LoC) technology has emerged as one of the major driving forces behind the recent surge in biochemical protocol automation. Dilution and mixture preparation with fluids in a desired ratio, constitute basic steps in sample preparation for which several LoC-based architectures and algorithms are known. The optimization of cost and time for such protocols requires proper sequencing of fluidic mix-and-split steps, and storage-units for holding intermediate-fluids to be reused in the later steps. However, practical design constraints often limit the amount of on-chip storage in microfluidic LoC architectures and thus can badly affect the performance of the algorithms. Consequently, results generated by previous work may not be useful (in the case they require more storage-units than available) or more expensive than necessary (in the case when storage-units are available but not used, e.g., to further reduce the number of mix/split operations or reactant-cost). In this paper, we propose new algorithms for dilution and mixing with continuous-flow-based LoCs that explicitly take care of storage constraints while optimizing reactant-cost and time of sample preparation. We present a symbolic formulation of the problem that captures the degree of freedom in algorithmic steps satisfying the specified storage constraints. Solvers based on Boolean satisfiability are used to achieve the optimization goals. The experimental results show the efficiency and effectiveness of the solution as well as a variety of applications where the proposed methods would prove beneficial.

Biochemical experiments, such as diagnostics must be precise and trusted, and provide quick time to results. This has been enabled by automated digital microfluidics; however, it also exposes these experiments to security threats. Previous work has shown that the critical challenge in securing digital microfluidic devices is the lack of sensing resources. The micro-electrode-dot-array (MEDA) is a next-generation digital microfluidic biochip platform that supports fine-grained control and real-time sensing of droplet movements. These capabilities permit continuous monitoring and checkpoint (CP)-based validation of assay execution on MEDA. This article presents a class of “shadow attacks” that abuse the timing slack in the assay execution. State-of-the-art CP-based validation techniques cannot expose the shadow operations. We overcome this limitation by introducing extra CPs in the assay execution at time instances when the assay is prone to shadow attacks. We achieve this by identifying the conditions that enable shadow attacks. We use these conditions to minimize the number of CPs required to guarantee the correctness of bioassay implementation. Our simulation results confirm the effectiveness and practicality of the defense.

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.

Sample preparation, an essential preprocessing step for biochemical protocols, is concerned with the generation of fluids satisfying specific target ratios and error-tolerance. Recent micro-electrode-dot-array (MEDA)-based DMF biochips provide the advantage of supporting both discrete and dynamic mixing models, the power of which has not yet been fully harnessed for implementing on-chip dilution and mixing of fluids. In this paper, we propose a novel factorization-based algorithm called FacDA for efficient and accurate dilution of sample fluid on a MEDA chip. Simulation results reveal that over a large number of test-cases with the mixing volume constraint in the range of 4--10 units, FacDA requires around 38% fewer mixing steps, 52% less sample units, and generates approximately 23% less wastage, all on average, compared to two prior dilution algorithms used for MEDA chips.

Researchers develop bioassays following rigorous experimentation in the lab that involves considerable fiscal and highly-skilled-person-hour investment. Previous work shows that a bioassay implementation can be reverse engineered by using images or video and control signals of the biochip. Hence, techniques must be devised to protect the intellectual property (IP) rights of the bioassay developer. This study is the first step in this direction and it makes the following contributions: (1) it introduces use of a sieve-valve as a security primitive to obfuscate bioassay implementations; (2) it shows how sieve-valves can be used to obscure biochip building blocks such as multiplexers and mixers; (3) it presents design rules and security metrics to design and measure obfuscated biochips. We assess the cost-security trade-offs associated with this solution and demonstrate practical sieve-valve based obfuscation on real-life biochips.

Researchers develop bioassays by rigorously experimenting in the lab. This involves significant fiscal and skilled person-hour investment. A competitor can reverse engineer a bioassay implementation by imaging or taking a video of a biochip when in use. Thus, there is a need to protect the intellectual property (IP) rights of the bioassay developer. We introduce a novel 3D multilayer-based obfuscation to protect a biochip against reverse engineering.

Digital microfluidic biochips (DMFBs) are now being extensively used to automate several biochemical laboratory protocols such as clinical analysis, point-of-care diagnostics, or DNA sequencing. In many biological assays, e.g., bacterial susceptibility tests and cellular response analysis, samples, or reagents are required in multiple concentration (or dilution) factors, satisfying certain gradient patterns such as linear, exponential, or parabolic. Dilution gradients are traditionally prepared using continuous-flow microfluidic devices. Unfortunately, most of them suffer from inflexibility and nonprogrammability, and they require large volumes of costly stock-solutions. DMFBs, on the other hand, are shown to produce, more efficiently, samples with multiple dilution factors. However, none of the existing DMFB-based algorithms utilize the properties of the gradient-profile while optimizing reactant-cost and sample-preparation time. In this paper, we explore the underlying combinatorial attributes of different gradients and harnessed them for efficient production of the desired concentration profile. For linear gradients, we present theoretical results concerning the number of mix-split operations and waste production, and prove an upper bound on on-chip storage requirement. A cost-effective method for generating a wide class of exponential gradients is also proposed. Finally, in order to handle a complex-shaped gradient, we posit a digital-geometric technique to approximate it with a sequence of linear gradients. Experimental results on various gradient-profiles are presented in support of the proposed method.

Sample preparation is a fundamental preprocessing step needed in almost all biochemical assays and is conveniently automated on a microfluidic lab-on-chip. In digital microfluidics, it is accomplished by a sequence of droplet-mix-split steps on a biochip. Many real-life applications require a sample with multiple concentration factors (CFs). Existing algorithms, while producing multi-CF targets, attempt to share the mix-split steps in order to reduce reactant-cost and sample-preparation time. However, all prior approaches have two limitations: 1) sharing of intermediate droplets can be best effected only when all required target CFs are known a priori and 2) the processing time may vary depending on the allowable error-tolerance in target-CFs. In this paper, we present a cost-effective solution to multi-CF-dilution on-demand, by using only one (or two) mix-split step(s). In order to service dynamically arriving requests of multiple CFs quickly, we prepare dilutions of the sample with a few CFs in advance (called source-CFs), and fill on-chip reservoirs with these fluids. For minimizing the number of such preprocessed CFs, we present an integer linear programming-based method, an approximation algorithm, and a heuristic algorithm. The proposed methods also allow the users to tradeoff the number of on-chip reservoirs against service time for various applications. Simulation results for several target sets demonstrate the superiority of the proposed techniques over prior art in terms of the number of mix-split steps, waste droplets, and reactant usage when the on-chip reservoirs are preloaded with source-CFs using a customized droplet-streaming engine.

Advancements in digital microfluidic biochip (DMFB) technologies are paving the way for low-cost and automated platforms for implementing bio-protocols. However, the deployment of DMFBs outside of controlled settings will make them vulnerable to intellectual property (IP) theft. Bio-protocol development requires large investments for cross-domain innovations in biochemical analysis, microfluidics, and cyberphysical systems. We propose a watermarking technique for bio-protocol IP protection-a first in microfluidics-that hierarchically embeds a secret signature across these domains. Such a signature can be exclusively attributed to the owner (like a hash). The proposed solution takes into account the inherent variability in domain-specific parameters such as mixing ratio, sensor calibration, and incubation time. We describe watermarking techniques of varying complexities for different bio-protocol steps. These include watermarking for bio-protocol synthesis parameters and the cyberphysical systems control path parameters. A watermarking scheme based on integer linear programming is proposed for the sample-preparation step of a bio-protocol. The practicality of our solution is demonstrated through case studies involving an immunoassay and several mixing ratios required in the sample-preparation process of bio-protocols. The effectiveness of this approach is evaluated through various security metrics: proof of ownership score, the probability of successful tampering of the watermark, and the probability of coincidence. We also analyze the integrity of the watermark against various possible attacks: brute force search, the insertion of a new watermark, and the watermarking of more parameters.

The fully programmable valve array (FPVA) is a general-purpose programmable flow-based microfluidic platform, akin to the VLSI field-programmable gate array (FPGA). FPVAs are dynamically reconfigurable and, hence, are suitable in a broad spectrum of applications involving immunoassays and cell analysis. Since these applications are safety critical, addressing security concerns is vital for the success and adoption of FPVAs. This study evaluates the security of FPVA biochips. We show that FPVAs are vulnerable to malicious operations similar to digital and flow-based microfluidic biochips. FPVAs are further prone to new classes of attacks-tunneling and deliberate aging. This study establishes security metrics and describes possible attacks on real-life bioassays. Furthermore, we study the use of machine learning (ML) techniques to detect and classify attacks based on the golden and real-time biochip state. In order to boost the classifier's performance, we propose a smart checkpointing mechanism. Experimental results are presented to showcase: 1) best-fit ML model classifier; 2) performance of different tradeoffs in checkpointing; and 3) effectiveness of the proposed smart checkpointing scheme.

Recent advances in microfluidics have been the major driving force behind the ubiquity of Labs-on-Chip (LoC) in biochemical protocol automation. The preparation of dilutions and mixtures of fluids is a basic step in sample preparation for which several algorithms and chip-architectures are well known. Dilution and mixing are implemented on biochips through a sequence of basic fluid-mixing and splitting operations performed in certain ratios. These steps are abstracted using a mixing graph. During this process, on-chip storage-units are needed to store intermediate fluids to be used later in the sequence. This allows to optimize the reactant-costs, to reduce the sample-preparation time, and/or to achieve the desired ratio. However, the number of storage-units is usually limited in given LoC architectures. Since this restriction is not considered by existing methods for sample preparation, the results that are obtained are often found to be useless (in the case when more storage-units are required than available) or more expensive than necessary (in the case when storage-units are available but not used, e.g., to further reduce the number of mixing operations or reactant-cost). In this paper, we present a storage-aware algorithm for sample preparation with flow-based LoCs which addresses these issues. We present a SAT-based approach to construct a mixing graph that enables the best usage of available storage-units while optimizing sample-preparation cost and/or time. Experimental results on several test cases reveal the scope, effectiveness, and the flexibility of the proposed method.

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.

The Micro-electrode-dot-array (MEDA) is a next-generation digital microfluidic biochip (DMFB) platform that supports fine-grained control and real-time sensing of droplet movements. These capabilities permit continuous monitoring and checkpoint-based validation of assay execution on MEDA. This paper presents a class of “shadow attacks” that abuse the timing slack in the assay execution. State-of-the-art checkpoint-based validation techniques cannot expose the shadow operations. We develop a defense that introduces extra checkpoints in the assay execution at time instances when the assay is prone to shadow attacks. Experiments confirm the effectiveness and practicality of the defense.

Microfluidic technology offers vast promise for implementing biochemistry-on-chip with diverse applications to clinical diagnosis, genome analysis, drug design, and point-of-care testing. Among various types of fluid-chips, droplet-based digital microfluidic biochips (DMFBs), which consist of a patterned array of controllable electrodes, provide the advantage of programmability, ease of fluidic operations, and versatile droplet mobility. However, because of manufacturing or field defects, electrode degradation, or dielectric breakdown, these chips may suffer from incorrect fluidic behavior. Reliability of fluidic operations is of utmost concern in DMFBs that are used to perform safety-critical bio-protocols. Various methods are deployed to test these devices, either offline or being overlapped with bioassay operations (termed as concurrent or in-field testing). The main challenge of in-field testing lies in the fact that the test must run concurrently with the execution of the normal assay without hampering the correctness of the latter. In prior work, optimal testing for droplet mobility over all electrodes was formulated in terms of finding either a Hamiltonian path or a Eulerian path in an undirected graph that represents the electrode-adjacency structure. Although these models have been studied for offline testing, no such effort was made in the area of concurrent testing. In this work, we propose, for in-field application, an SAT-based modeling and solution approach to find an optimal test plan that can be used to check droplet movement across the boundary between every pair of adjacent electrodes, which is visited by the droplets of the ongoing assay. The proposed method is robust and determines a test solution successfully regardless of the cover assay that is being executed concurrently. Experiments on several real-life assays and other test cases demonstrate the effectiveness of the method with respect to test completion time.

Sample preparation plays a crucial role in almost all biochemical applications, since a predominant portion of biochemical analysis time is associated with sample collection, transportation, and preparation. Many sample-preparation algorithms are proposed in the literature that are suitable for execution on programmable digital microfluidic (DMF) platforms. In most of the existing DMF-based sample-preparation algorithms, a fixed target ratio is provided as input, and the corresponding mixing tree is generated as output. However, in many biochemical applications, target mixtures with exact component proportions may not be needed. From a biochemical perspective, it may be sufficient to prepare a mixture in which the input reagents may lie within a range of concentration factors. The choice of a particular valid ratio, however, strongly impacts solution-preparation cost and time. To address this problem, we propose a concentration-resilient ratio-selection method from the input ratio space so that the reactant cost is minimized. We propose an integer linear programming--based method that terminates very fast while producing the optimum solution, considering both uniform and weighted cost of reagents. Experimental results reveal that the proposed method can be used conveniently in tandem with several existing sample-preparation algorithms for improving their performance.

Advances in digital microfluidic (DMF) technologies offer a promising platform for a variety of biochemical applications, ranging from massively parallel DNA analysis and computational drug discovery to toxicity monitoring and medical diagnosis. In this paper, we address the migration problem that arises when the technology undergoes a change in the context of DMFs. Given a biochemical reaction synthesized for actuation on a given DMF architecture, we discuss how the same biochemical reaction can be ported seamlessly to an enhanced architecture, with possible modifications to the architectural parameters (e.g., clock frequency, mixer size, and mixing time) or geometric changes (e.g., change in reservoir locations or mixer positions, inclusion of new sensors or other physical resources). Complete resynthesis of the protocol for the new architecture may often become either inefficient or even infeasible due to scalability, proprietary, security, or cost issues. We propose an adaptation method for handling such technology-changes by modifying the existing actuation sequence through an incremental procedure. The foundation of our method lies in symbolic encoding and satisfiability-solvers, enriched with pertinent graph-theoretic and geometric techniques. This enables us to generate functionally correct solutions for the new target architecture without necessitating a complete resynthesis step, thereby enabling the utilization of these chips by users in biology who are not familiar with the on-chip synthesis tool-flow. We highlight the benefits of the proposed approach through extensive simulations on assay benchmarks.

Albeit sample preparation is well-studied for digital microfluidic biochips, very few prior work addressed this problem in the context of continuous-flow microfluidics from an algorithmic perspective. In the latter class of chips, microvalves and micropumps are used to manipulate on-chip fluid flow through microchannels in order to execute a biochemical protocol. Dilution of a sample fluid is a special case of sample preparation, where only two input reagents (commonly known as sample and buffer) are mixed in a desired volumetric ratio. In this paper, we propose a satisfiability-based dilution algorithm assuming the generalized mixing models supported by an N-segment, continuous-flow, rotary mixer. Given a target concentration and an error limit, the proposed algorithm first minimizes the number of mixing operations, and subsequently, reduces reagent-usage. Simulation results demonstrate that the proposed method outperforms existing dilution algorithms in terms of mixing steps (assay time) and waste production, and compares favorably with respect to reagent-usage (cost) when 4- and 8-segment rotary mixers are used. Next, we propose two variants of an algorithm for handling the open problem of k-reagent mixture-preparation (k ≥ 3) with an N-segment continuous-flow rotary mixer, and report experimental results to evaluate their performance. A software tool called flow-based sample preparation algorithm has also been developed that can be readily used for running the proposed algorithms.

In many biochemical protocols, sample preparation is an extremely important step for mixing multiple reagents in a given ratio. Dilution of a biochemical sample/reagent is the special case of mixing or solution preparation where only two fluids (sample and buffer) are mixed at a certain ratio corresponding to the desired concentration factor. Many bioassays often require multiple concentration values of the same sample/reagent, and implementing them efficiently on a digital microfluidic biochip is a challenge. In this paper, we present an algorithmic solution for the problem of producing a set of different target droplets in a minimum number of mix-split steps, and satisfying a given upper bound in concentration error. Unlike prior methods, this approach does not require any intermediate storage. We represent the underlying search space using a binary de Brujin graph and show that a shortest mix-split sequence can be obtained by solving an asymmetric traveling salesman problem therein. Simulation results over a large data set reveal that the proposed technique outperforms existing methods in terms of the number of mix-split steps, waste droplets, and reactant usage. The method is applicable in general scenarios of either one mixer or more mixers on the chip. A digital microfluidic platform can be easily designed to implement such a technique for rapid on-chip sample preparation.