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Flow-based microfluidic biochips (FMBs) have been rapidly commercialized and deployed in recent years for biological computing, clinical diagnostics, and point-of-care-tests (POCTs). However, outsourcing FMBs makes them susceptible to material-level attacks by malicious actors for illegitimate monetary gain. The attacks involve deliberate material degradation of an FMB's polydimethylsiloxane (PDMS) components by either doping with reactive solvents or altering the PDMS curing ratio during fabrication. Such attacks are stealthy enough to evade detection and deteriorate the FMB's function. Furthermore, material-level attacks can become prevalent in attacks based on intellectual property (IP) theft, such as counterfeiting, overbuilding, etc. , which involve unscrupulous third-party manufacturers. To address this problem, we present a dynamic material-level watermarking scheme for PDMS-based FMBs with microvalves using a perylene-labeled fluorescent dye. The dyed microvalves show a unique excimer intensity peak under 405 nm laser excitation. Moreover, when pneumatically actuated, the peak shows a predetermined downward shift in intensity as a function of mechanical strain. We validated this protection scheme experimentally using fluorescence microscopy, which showed a high correlation ( R 2 = 0.971) between the normalized excimer intensity change and the maximum principal strain of the actuated microvalves. To detect curing ratio-based attacks, we adapted machine learning (ML) models, which were trained on the force-displacement data obtained from a mechanical punch test method. Our ML models achieved more than 99% accuracy in detecting curing ratio anomalies. These countermeasures can be used to proactively safeguard FMBs against material-level attacks in the era of global pandemics and diagnostics based on POCTs.

Flow-based microfluidic biochips (FMBs) have seen rapid commercialization and deployment in recent years for point-of-care and clinical diagnostics. However, the outsourcing of FMB design and manufacturing makes them susceptible to susceptible to malicious physical level and intellectual property (IP)-theft attacks. This work demonstrates the first structure-based (SB) attack on representative commercial FMBs. The SB attacks maliciously decrease the heights of the FMB reaction chambers to produce false-negative results. We validate this attack experimentally using fluorescence microscopy, which showed a high correlation ( R 2 = 0.987) between chamber height and related fluorescence intensity of the DNA amplified by polymerase chain reaction. To detect SB attacks, we adopt two existing deep learning-based anomaly detection algorithms with  ∼ 96% validation accuracy in recognizing such deliberately introduced microstructural anomalies. To safeguard FMBs against intellectual property (IP)-theft, we propose a novel device-level watermarking scheme for FMBs using intensity-height correlation. The countermeasures can be used to proactively safeguard FMBs against SB and IP-theft attacks in the era of global pandemics and personalized medicine.

Contact-free manipulation of small objects (e.g., cells, tissues, and droplets) using acoustic waves eliminates physical contact with structures and undesired surface adsorption. Pioneering acoustic-based, contact-free manipulation techniques (e.g., acoustic levitation) enable programmable manipulation but are limited by evaporation, bulky transducers, and inefficient acoustic coupling in air. Herein, we report an acoustofluidic mechanism for the contactless manipulation of small objects on water. A hollow-square-shaped interdigital transducer (IDT) is fabricated on lithium niobate (LiNbO3), immersed in water and used as a sound source to generate acoustic waves and as a micropump to pump fluid in the ±x and ±y orthogonal directions. As a result, objects which float adjacent to the excited IDT can be pushed unidirectionally (horizontally) in ±x and ±y following the directed acoustic wave propagation. A fluidic processor was developed by patterning IDT units in a 6-by-6 array. We demonstrate contactless, programmable manipulation on water of oil droplets and zebrafish larvae. This acoustofluidic-based manipulation opens avenues for the contactless, programmable processing of materials and small biosamples.

Digital microfluidic biochips (DMFBs) are being increasingly used for DNA sequencing, point-of-care clinical diagnostics, and immunoassays. DMFBs based on a micro-electrode-dot-array (MEDA) architecture have recently been proposed, and fundamental droplet manipulations, e.g., droplet mixing and splitting, have also been experimentally demonstrated on MEDA biochips. There can be thousands of microelectrodes on a single MEDA biochip, and the fine-grained control of nanoliter volumes of biochemical samples and reagents is also enabled by this technology. MEDA biochips offer the benefits of real-time sensitivity, lower cost, easy system integration with CMOS modules, and full automation. This review paper first describes recent design tools for high-level synthesis and optimization of map bioassay protocols on a MEDA biochip. It then presents recent advances in scheduling of fluidic operations, placement of fluidic modules, droplet-size-aware routing, adaptive error recovery, sample preparation, and various testing techniques. With the help of these tools, biochip users can concentrate on the development of nanoscale bioassays, leaving details of chip optimization and implementation to software tools.

Sample preparation in digital microfluidics refers to the generation of droplets with target concentrations for on-chip biochemical applications. In recent years, digital microfluidic biochips (DMFBs) have been adopted as a platform for sample preparation. However, there remain two major problems associated with sample preparation on a conventional DMFB. First, only a (1:1) mixing/splitting model can be used, leading to an increase in the number of fluidic operations required for sample preparation. Second, only a limited number of sensors can be integrated on a conventional DMFB; as a result, the latency for error detection during sample preparation is significant. To overcome these drawbacks, we adopt a next generation DMFB platform, referred to as micro-electrode-dot-array (MEDA), for sample preparation. We propose the first sample-preparation method that exploits the MEDA-specific advantages of fine-grained control of droplet sizes and real-time droplet sensing. Experimental demonstration using a fabricated MEDA biochip and simulation results highlight the effectiveness of the proposed sample-preparation method.

A digital microfluidic biochip (DMFB) is an attractive technology platform for automating laboratory procedures in biochemistry. In recent years, DMFBs based on a microelectrode-dot-array (MEDA) architecture have been demonstrated. However, due to the inherent differences between today's DMFBs and MEDA, existing synthesis solutions for biochemistry mapping cannot be utilized for MEDA biochips. We present the first synthesis approach that can be used for MEDA biochips. We first present a general analytical model for droplet velocity and validate it experimentally using a fabricated MEDA biochip. We then present the proposed synthesis method targeting reservoir placement, operation scheduling, module placement, routing of droplets of various sizes, and diagonal movement of droplets in a two-dimensional array. Simulation results using benchmarks and experimental results using a fabricated MEDA biochip demonstrate the effectiveness of the proposed synthesis technique.

Recent advances in microfluidics technology have led to the emergence of miniaturized biochip devices, also referred to as lab-on-a-chip, for biochemical analysis. A promising category of microfluidic biochips relies on the principle of electrowetting-on-dielectric, whereby discrete droplets of nanoliter volumes can be manipulated using an array of electrodes. As chemists adapt more bioassays for concurrent execution on such ldquodigitalrdquo droplet-based microfluidic platforms, system integration, design complexity, and the need for defect tolerance are expected to increase rapidly. Automated design tools for defect-tolerant and multifunctional biochips are important for the emerging marketplace, especially for low-cost, portable, and disposable devices for clinical diagnostics. We present a unified synthesis method that combines defect-tolerant architectural synthesis with defect-aware physical design. The proposed approach allows architectural-level design choices and defect-tolerant physical design decisions to be made simultaneously. We use a large-scale protein assay and the polymerase chain reaction procedure as case studies to evaluate the proposed synthesis method. We also carry out simulations based on defect injection to evaluate the robustness of the synthesized biochip designs. [ABSTRACT FROM PUBLISHER]

Dependability is an important attribute for microfluidic biochips that are used for safety-critical applications such as point-of-care health assessment, air-quality monitoring, and food-safety testing. Therefore, these devices must be adequately tested after manufacture and during bioassay operations. We propose a parallel scan-like testing methodology for digital microfluidic devices. A diagnosis method based on test outcomes is also proposed. The diagnosis technique is enhanced such that multiple defect sites can be efficiently located using parallel scan-like testing. Defect diagnosis can be used to reconfigure a digital microfluidic biochip such that faults can be avoided, thereby enhancing chip yield and defect tolerance. We evaluate the proposed method using complexity analysis as well as applying it to a fabricated biochip. [ABSTRACT FROM PUBLISHER]

Recent events have heightened the need for fast, accurate, and reliable biological/chemical sensor systems for critical locations. As droplet-based microelectrofluidic sensor systems become widespread in these safety-critical biomedical applications, reliability emerges as a critical performance parameter. In order to ensure the operational health of such safety-critical systems, they need to be monitored for defects, not only after manufacturing, but also during in-field operation. In this paper, we present a cost-effective concurrent test methodology for droplet-based microelectrofluidic systems. We present a classification of catastrophic and parametric faults in such systems and show how faults can be detected by electrostatically controlling and tracking droplet motion. We then present a fault simulation approach based on tolerance analysis using Monte-Carlo simulation to characterize the impact of parameter variations on system performance. Finally, we present experimental results on a droplet-based microelectrofluidic system for a real-time polymerase chain reaction application. [ABSTRACT FROM PUBLISHER]