Digital Microfluidic Biochips Research Articles

Extending the Lifetime of MEDA Biochips by Selective Sensing on Microelectrodes

A digital microfluidic biochip (DMFB) enables miniaturization of immunoassays, point-of-care clinical diagnostics, and DNA sequencing. A recent generation of DMFBs uses a micro-electrode-dot-array (MEDA) architecture, which provides fine-grained control of droplets and real-time droplet sensing using the CMOS technology. However, microelectrodes in a MEDA biochip degrade when they are charged and discharged frequently during bioassay execution. In this article, we first make the key observation that the droplet-sensing operations contribute up to 94% of all microelectrode actuation in MEDA. Consequently, to reduce the number of droplet-sensing operations, we present a new microelectrode cell (MC) design as well as a selective-sensing method such that only a small fraction of microelectrodes perform droplet sensing during bioassay execution. The selection of microelectrodes that need to perform the droplet sensing is based on an analysis of experimental data. A comprehensive set of simulation results show that the total number of droplet-sensing operations is reduced to only 0.7%, which prolongs the lifespan of a MEDA biochip by 11× without any impact on bioassay time-to-response.

Hardware Design and Fault-Tolerant Synthesis for Digital Acoustofluidic Biochips.

A digital microfluidic biochip (DMB) is an attractive platform for automating laboratory procedures in microbiology. To overcome the problem of cross-contamination due to fouling of the electrode surface in traditional DMBs, a contactless liquid-handling biochip technology, referred to as acoustofluidics, has recently been proposed. A major challenge in operating this platform is the need for a control signal of frequency 24MHz and voltage range ±10/±20 V to activate the IDT units in the biochip. In this paper, we present a hardware design that can efficiently activate/de-activated each IDT, and can fully automate an bio-protocol. We also present a fault-tolerant synthesis technique that allows us to automatically map biomolecular protocols to acoustofluidic biochips. We develop and experimentally validate a velocity model, and use it to guide co-optimization for operation scheduling, module placement, and droplet routing in the presence of IDT faults. Simulation results demonstrate the effectiveness of the proposed synthesis method. Our results are expected to open new research directions on design automation of digital acoustofluidic biochips.

An efficient module-less synthesis approach for Digital Microfluidic Biochip.

Digital Microfluidic Biochips (DMFBs) will require error-free synthesis techniques which can function at much higher speed while implementing on real-time systems and capable of tackling more complex assay operations. Until now various bio-assays are successfully implemented based on different mixing modules present on such lab-on-chips. In present work, the concept of such dedicated virtual modules has been eliminated and a novel module-less-synthesis (MLS) method is proposed for accomplishing high-performance bio-protocols. Various shift-patterns (movements) of the micro-droplets are identified to accomplish entire mixing in lesser time compared to earlier module-based synthesis methods. We have also computed the percentage of mixing accomplishment for each directional-shift of the mixer-droplet. However, path congestion problem and operational errors are inevitable in MLS approach. Hence, the path congestion and washing problem in MLS is addressed by tweaking the earlier MLS approach and a new modified-MLS (MMLS) method is proposed. Finally, washing optimization technique on MMLS method is also given. Different real-life bio assays like PCR, IVD are tested with the proposed technique as well as synthetic benchmarks (hard test benches) are also incorporated in the experiments. For both kind of benchmarks synthesis performance improved with bioassay completion time (T_{mathrm{max}}) significantly reduced compared to existing synthesis approaches on DMFB platform.

Architectural Design of Flow-Based Microfluidic Biochips for Multi-Target Dilution of Biochemical Fluids

Microfluidic technologies enable replacement of time-consuming and complex steps of biochemical laboratory protocols with a tiny chip. Sample preparation (i.e., dilution or mixing of fluids) is one of the primary tasks of any bioprotocol. In real-life applications where several assays need to be executed for different diagnostic purposes, the same sample fluid is often required with different target concentration factors ( CF s). Although several multi-target dilution algorithms have been developed for digital microfluidic biochips, they are not efficient for implementation with continuous-flow-based microfluidic chips, which are preferred in the laboratories. In this article, we present a multi-target dilution algorithm ( MTDA ) for continuous-flow-based microfluidic biochips, which to the best of our knowledge is the first of its kind. We design a flow-based rotary mixer with a suitable number of segments depending on the target- CF profile, error tolerance, and optimization criteria. To schedule several intermediate fluid-mixing tasks, we develop a multi-target scheduling algorithm ( MTSA ) aiming to minimize the usage of storage units while producing dilutions with multiple CF s. Furthermore, we propose a storage architecture for efficiently loading (storing) of intermediate fluids from (to) the storage units.

Programmable Daisychaining of Microelectrodes to Secure Bioassay IP in MEDA Biochips

As digital microfluidic biochips (DMFBs) make the transition to the marketplace for commercial exploitation, security and intellectual property (IP) protection are emerging as important design considerations. Recent studies have shown that DMFBs are vulnerable to reverse engineering aimed at stealing biomolecular protocols (IP theft). The IP piracy of proprietary protocols may lead to significant losses for pharmaceutical and biotech companies. The microelectrode dot array (MEDA) is a next-generation DMFB platform that supports real-time sensing of droplets and has the added advantage of important security protection. However, real-time sensing offers opportunities to an attacker to steal the biochemical IP. We show that the daisychaining of microelectrodes and the use of one-time programmability in MEDA biochips provides effective bitstream scrambling of biochemical protocols. To examine the strength of this solution, we develop a Satisfiability (SAT)-based attack that can unscramble the bitstreams through repeated observations of bioassays executed on the MEDA platform. Based on insights gained from the SAT attack, we propose an advanced defense against IP theft. Simulation results using real-life biomolecular protocols confirm that while the SAT attack is effective for simple instances, our advanced defense can thwart it for realistic MEDA biochips and real-life protocols.

Security of Microfluidic Biochip

With the advancement of system miniaturization and automation, Lab-on-a-Chip (LoC) technology has revolutionized traditional experimental procedures. Microfluidic Biochip (MFB) is an emerging branch of LoC with wide medical applications such as DNA sequencing, drug delivery, and point of care diagnostics. Due to the critical usage of MFBs, their security is of great importance. In this article, we exploit the vulnerabilities of two types of MFBs: Flow-based Microfluidic Biochip (FMFB) and Digital Microfluidic Biochip (DMFB). We propose a systematic framework for applying Reverse Engineering (RE) attacks and Hardware Trojan (HT) attacks on MFBs as well as for practical countermeasures against the proposed attacks. We evaluate the attacks and defense on various benchmarks where experimental results prove the effectiveness of our methods. Security metrics are defined to quantify the vulnerability of MFBs. The overhead and performance of the proposed attacks as well as countermeasures are also discussed.

How Secure Are Checkpoint-Based Defenses in Digital Microfluidic Biochips?

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.

IJTAG-Based Fault Recovery and Robust Microelectrode-Cell Design for MEDA Biochips

A digital microfluidic biochip (DMFB) is an attractive platform for immunoassays, point-of-care clinical diagnostics, DNA sequencing, and other laboratory procedures in biochemistry. A recent generation of biochips uses a micro-electrode-dot-array (MEDA) architecture, which provides fine-grained controllability of droplets and seamlessly integrates microelectronics and microfluidics using CMOS technology. In order to ensure robust fluidic operations and high confidence in the outcome of biochemical experiments, chip testing, fault diagnosis, and fault recovery are critical for MEDA biochips. In this article, we present an effective fault-recovery solution based on the homogeneous structure of MEDA. Since the microelectrode cells (MCs) in an MEDA biochip are identical, we add multiplexers for reconfigurability, whereby an MC with faulty components can use the hardware resources in a neighboring MC. In addition, we use the IEEE 1687 (also known as IJTAG) network to reduce the number of control signals needed for the multiplexers, and to provide flexible subscan chain access for the fault-recovery control flow. A comprehensive set of simulation results demonstrates the effectiveness of the proposed fault-recovery solution for MEDA biochips.

Chip level design in MEDA based biochips: application of daisy chain based actuation

Recent advances in microfluidics and micro-fabrication technology enabled the emergence of a new microelectrode-dot-array (MEDA) architecture for microfluidic biochips. The MEDA based design facilitates dynamic routing with variable sized droplets having advanced characteristics such as field programmability, higher level of reconfigurability and scalability. Unlike traditional digital microfluidic biochips, droplet actuations for MEDA based biochips are accomplished through a daisy chain based interconnection of the microelectrodes. This paper provides a detail analysis of daisy chain configuration for different conditions e.g. manipulation of single droplets as well as manipulation of multiple droplets. A daisy chain control sequence for actuation has been proposed in order to evade droplet interference. This paper also defines different actuation sequences for a set of fundamental microfluidic operations namely droplet merging, mixing, splitting and transportation essential for bioassay execution. Finally a daisy chain actuation sequence for overall execution of a specified bioassay from sample preparation to chip level design are demonstrated. Experimental results figure out the daisy chain actuation time for implementation of standard test benches.

An Efficient Algorithm for Optimizing the Test Path of Digital Microfluidic Biochips

Digital microfluidic biochips (DMFBs) have been widely used in biochemical experiments with high safety requirements. To ensure the reliability of the experiment, it is necessary to use test droplets to perform off-line and on-line testing for DMFBs. Previous random search algorithms are not fully combined with heuristic information, which increases the randomness of search progress and thus results in suboptimal test paths. To solve this problem, a new test path optimization method based on priority strategy and genetic algorithm is proposed, which reduces the blindness of test path search and improves the convergence effect of the random search algorithm. In this method, priority levels are randomly assigned to the edges of the chip test model. With the fluid constraints, a test droplet moves to the untraversed adjacent edge with the highest priority. If all adjacent edges have been traversed, Floyd algorithm is used to determine the shortest path from the test droplet to untraversed edges, and guide the test droplet to move along the shortest path. After determining the test path according to priority strategy, the priority level on the test path is optimized by genetic algorithm, so that the length of the path is gradually reduced by iteration. In this paper, a single test droplet is used to test given chips. The experimental results show that the proposed algorithm is efficient, and the shortest path length is equal to the length of the Euler path, indicating that the shortest test path has reached the optimal value. Moreover, for the “deadlock” problem of droplets in on-line testing process, we also provide a solution by using backoff operation.

Secure Assay Execution on MEDA Biochips to Thwart Attacks Using Real-Time Sensing

Digital microfluidic biochips (DMFBs) have emerged as a promising platform for DNA sequencing, clinical chemistry, and point-of-care diagnostics. Recent research has shown that DMFBs are susceptible to various types of malicious attacks. Defenses proposed thus far only offer probabilistic guarantees of security due to the limitation of on-chip sensor resources. A micro-electrode-dot-array (MEDA) biochip is a next-generation DMFB that enables the real-time sensing of on-chip droplet locations, which are captured in the form of a droplet-location map. We propose a security mechanism that validates assay execution by reconstructing the sequencing graph (i.e., the assay specification) from the droplet-location maps and comparing it against the golden sequencing graph. We prove that there is a unique (one-to-one) mapping from the set of droplet-location maps (over the duration of the assay) to the set of possible sequencing graphs. Any deviation in the droplet-location maps due to an attack is detected by this countermeasure because the resulting derived sequencing graph is not isomorphic to the original sequencing graph. We highlight the strength of the security mechanism by simulating attacks on real-life bioassays. We also address the concern that the proposed mechanism may raise false alarms when some fluidic operations are executed on MEDA biochips. To avoid such false alarms, we propose an enhanced sensing technique that provides fine-grained sensing for the security mechanism.

Synthesis of Tamper-Resistant Pin-Constrained Digital Microfluidic Biochips

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.

Pin Addressing Method Based on an SVM With a Reliability Constraint in Digital Microfluidic Biochips

Digital microfluidic biochips (DMFBs) are increasingly important and are used for point-of-care, drug discovery, clinical diagnosis, immunoassays, etc. Pin-constrained DMFBs are an important part of digital microfluidic biochips, and they have gained increasing attention from researchers. However, many previous works have focused on the problem of electrode addressing and aimed to minimize the number of control pins in pin-constrained DMFBs. Although the number of control pins can be effectively redistributed through broadcast addressing technology, the chip reliability will be reduced if the signals are shared arbitrarily. Arbitrary signal sharing can lead to a large number of actuations for many idle electrodes, and as a result, a trapping charge or decreasing contact angle problem could occur for some electrodes, reducing the reliability of the chip. To address this problem, the appropriate electrode matching object should be carefully selected, and the influence of these factors on chip reliability should be fully considered. For this purpose, we aimed to fully consider electrode addressing and the reliability of the chip in improving the reliability of DMFBs. This paper proposed a pin addressing method based on a support vector machine (SVM) with the reliability constraint algorithm, which can fully consider the electrode addressing method and the reliability of the chip together. The proposed method achieved an average maximum number of electrode actuations that was 53.8% and 18.2% smaller than those of the baseline algorithm and the graph-based algorithm, respectively. The simulation experiment results showed that the proposed method can efficiently solve reliability problems during the DMFB design process.

Unified Contamination-Aware Routing Method Considering Realistic Washing Capacity Constraint in Digital Microfluidic Biochips

To fully utilize the dynamic reconfigurability of digital microfluidic biochips, most of electrodes would be shared by different droplets. Thus, contaminations caused by liquid residues among droplets are inevitable which lead to lethal errors in bioassays. To remove the contaminations, washing operations are introduced as an essential step to ensure the correctness of bioassay. However, existing works have oversimplified assumptions on the washing droplet's behavior and constraints which cannot clean all contaminations with erroneous outcomes. Moreover, straightforward integration of washing operations with droplet routing may increase the execution time of a bioassay which is not feasible for timing-critical bioassay. To effectively remove contaminations and minimize the execution time of a bioassay, this article proposes a unified contamination-aware routing method, which addresses the above issues simultaneously. Firstly, we present a top-down scheme to generate candidates of routing paths, then construct a shortest-path model to select desirable routing solution for all subproblems. With a decision diagram of droplets, we further propose an integer linear programming (ILP) formulation to compact the execution time. Finally, contamination removal by washing droplets with realistic washing capacity is considered for all subproblems. Tested on real-life benchmarks, our proposed method can significantly reduce 72% contamination spots and save 11% execution time.

Toward Secure Microfluidic Fully Programmable Valve Array Biochips

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.

Bio-chemical Assay Locking to Thwart Bio-IP Theft

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.

Bio-Protocol Watermarking on Digital Microfluidic Biochips

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.

A High-performance Homogeneous Droplet Routing Technique for MEDA-based Biochips

Recent advancement of microelectrode-dot-array (MEDA)-based architecture for digital microfluidic biochips has enabled a major enhancement in microfluidic operations for traditional lab-on-chip devices. One critical issue for MEDA-based biochips is the transportation of droplets. MEDA allows dynamic routing for droplets of different size. In this article, we propose a high-performance droplet routing technique for MEDA-based digital microfluidic biochips. First, we propose the basic concept of droplet movement strategy in MEDA-based design together with a definition of strictly shielded zones within the layout in MEDA architecture. Next, we propose transportation schemes of droplets for MEDA architecture under different blockage or crossover conditions and estimate route distances for each net in offline. Finally, a priority-based routing strategy combining various transportation schemes stated earlier has been proposed. Concurrent movement of each droplet is scheduled in a time-multiplexed manner. This poses critical challenges for parallel routing of individual droplets with optimal sharing of cells formulating a routing problem with higher complexity. The final compaction solution satisfies the timing constraint and improves fault tolerance. Simulations are carried out on standard benchmark circuits, namely, Benchmark suite I and Benchmark suite III. Experimental results show satisfactory improvements and prove a high degree of robustness for our proposed algorithm.

A particle swarm optimization method for fault localization and residue removal in digital microfluidic biochips

Second generation microfluidic biochip is known as digital microfluidic biochip (DMFB). DMFB performs different clinical pathological experimentation, DNA sequencing, air contamination detection and many other bio-chemical experiments based on appropriate bio-assay protocols. DMFB comprises two dimensional array of electrodes fabricated over two parallel glass plates, which is capable of performing miniaturized (nano/ pico liter volume) droplet dispensing, transportation and mixing through electro-wetting of dielectrics (EWOD). Droplet operations through EWOD are mostly managed by droplet scheduling algorithms which are NP hard in nature. At present reliability is a major concern for commercialization of operational DMFB. Reliable output of DMFB is mostly affected by different faults within electrodes. This further suffers from cross-contamination issues among different droplets due to repeated use of DMFB boards for different assay operations. Present work proposes a novel test droplet routing method based on adaptive weighted particle swarm optimization (PSO) model. This test droplet circulation method aims to identify defective electrodes and simultaneously performs residue removal. Experimental findings of the proposed model on some standard test benches and real life bio-assay samples reveal operational supremacy in terms of overall computational time and operational accuracy over some existing best known models.

Microfluidic Design for Concentration Gradient Generation Using Artificial Neural Network

According to the complexity of human physiology and variability among individuals, e.g., genes, environment, lifestyle exposures, etc., personalized medicine aims to synthesize the specific efficacious drug for each individual patient. For synthesizing personalized medicine, customized solutions with specific concentrations are required. Equipped with the advantages in saving costly reagents and rare samples, microfluidic biochips are promising in generating different concentrations for personalized medicine. On the one hand, digital microfluidic biochips require the programming control for driving the movement of the droplets, which suffer from random errors caused by imbalanced droplet splitting. On the other hand, existing flow-based microfluidic biochips can only generate linear concentration gradients, which cause significant waste for synthesizing personalized medicine. To address the above issues, this article proposes the first artificial neural network (ANN)-based design method for flow-based microfluidic biochips, which accurately generates the customized concentration gradients. According to the required concentration, an initial chip is first selected from the prebuilt database and then fine-tuned by ANN to better match the required concentration. The computational simulation results show that the induced deviations in generated concentrations are generally less than 0.014, which validates the accuracy of the proposed neural network model.