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.

Keynote Paper: From EDA to IoT eHealth: Promises, Challenges, and Solutions

A digital microfluidic biochip (DMFB) is an attractive technology platform for automating laboratory procedures in biochemistry. However, today's DMFBs suffer from several limitations: (i) constraints on droplet size and the inability to vary droplet volume in a fine-grained manner; (ii) the lack of integrated sensors for real-time detection; (iii) the need for special fabrication processes and reliability/yield concerns. To overcome the above problems, DMFBs based on a micro-electrode-dot-array (MEDA) architecture have recently been demonstrated. However, due to the inherent differences between today's DMFBs and MEDA, existing synthesis solutions cannot be utilized for MEDA-based biochips. We present the first biochip synthesis approach that can be used for MEDA. The proposed synthesis method targets 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 co-optimization technique.

High-level synthesis for micro-electrode-dot-array digital microfluidic biochips

Continuous-flow microfluidics rapidly evolved in the last decades as a solution to automate laboratory procedures in molecular biology and biochemistry. Therefore, the physical design of the corresponding chips, i.e., the placement and routing of the involved components and channels, received significant attention. Recently, several physical design solutions for this task have been presented. However, they often rely on general heuristics which traverse the search space in a rather arbitrary fashion and, additionally, consider placement and routing independently from each other. Consequently, the obtained results are often far from being optimal. In this work, a methodology is proposed which aims for determining close-to-optimal physical designs for continuous-flow microfluidic biochips. To this end, we consider all — or, at least, as much as possible — of the valid solutions. As this obviously yields a significant complexity, solving engines are utilized to efficiently traverse the search space and pruning schemes are proposed to reduce the search space without discarding too many promising solutions. Evaluations show that the proposed methodology is capable of determining optimal results for small experiments to be realized. For larger experiments, close-to-optimal results can efficiently be derived. Moreover, compared to the current state-of-the-art, improvements of up to 1–2 orders of magnitude can be observed.

Close-to-optimal placement and routing for continuous-flow microfluidic biochips

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.

Droplet Size-Aware High-Level Synthesis for Micro-Electrode-Dot-Array Digital Microfluidic Biochips

Recent advances in flow-based microfluidic biochips have enabled the emergence of lab-on-a-chip devices for bimolecular recognition and point-of-care disease diagnostics. However, the adoption of flow-based biochips is hampered today by the lack of computer-aided design tools. Manual design procedures not only delay product development but they also inhibit the exploitation of the design complexity that is possible with current fabrication techniques. In this paper, we present the first practical problem formulation for automated control-layer design in flow-based microfluidic very large-scale integration (mVLSI) biochips and propose a systematic approach for solving this problem. Our goal is to find an efficient routing solution for control-layer design with a minimum number of control pins. The pressure-propagation delay, an intrinsic physical phenomenon in mVLSI biochips, is minimized in order to reduce the response time for valves, decrease the pattern set-up time, and synchronize valve actuation. Two fabricated flow-based devices and six synthetic benchmarks are used to evaluate the proposed optimization method. Compared with manual control-layer design and a baseline approach, the proposed approach leads to fewer control pins, better timing behavior, and shorter channel length in the control layer.

Control-Layer Routing and Control-Pin Minimization for Flow-Based Microfluidic Biochips

Recent advances in flow-based microfluidics have led to the emergence of biochemistry-on-a-chip as a new paradigm in clinical diagnostics and biomolecular recognition. However, a potential roadblock in the deployment of microfluidic biochips is the lack of test techniques to screen defective devices before they are used for biochemical analysis. Defective chips lead to repetition of experiments, which is undesirable due to high reagent cost and limited availability of samples. Prior work on fault detection in biochips has been limited to digital (“droplet”) microfluidics and other electrode-based technology platforms. The paper proposes the first approach for automated testing of flow-based microfluidic biochips that are designed using membrane-based valves for flow control. The proposed test technique is based on a behavioral abstraction of physical defects in microchannels and valves. The flow paths and flow control in the microfluidic device are modeled as a logic circuit composed of Boolean gates, which allows test generation to be carried out using standard automatic test pattern generation tools. The tests derived using the logic circuit model are then mapped to fluidic operations involving pumps and pressure sensors in the biochip. Feedback from pressure sensors can be compared to expected responses based on the logic circuit model, whereby the types and positions of defects are identified. We show how a fabricated biochip can be tested using the proposed method, and demonstrate experimental results for two additional fabricated chips.

https://ir.nctu.edu.tw:443/bitstream/11536/123974/1/000344529700003.pdf

Testing of Flow-Based Microfluidic Biochips: Fault Modeling, Test Generation, and Experimental Demonstration

Advances in digital microfluidics and integrated sensing hold promise for a new generation of droplet-based biochips that can perform multiplexed assays to determine the identity of target molecules. Despite these benefits, defects and erroneous fluidic operations remain a major barrier to the adoption and deployment of these devices. We describe the first integrated demonstration of cyberphysical coupling in digital microfluidics, whereby errors in droplet transportation on the digital microfluidic platform are detected using capacitive sensors, the test outcome is interpreted by control hardware, and software-based error recovery is accomplished using dynamic reconfiguration. The hardware/software interface is realized through seamless interaction between control software, an off-the-shelf microcontroller and a frequency divider implemented on an FPGA. Experimental results are reported for a fabricated silicon device and links to videos are provided for the first-ever experimental demonstration of cyberphysical coupling and dynamic error recovery in digital microfluidic biochips.

Fault detection, real-time error recovery, and experimental demonstration for digital microfluidic biochips

Digital (droplet-based) microfluidics enables the integration of fluid-handling operations and reaction-outcome detection. Despite these benefits, defects and erroneous fluidic operations continue to be major barriers to the adoption and deployment of these devices. We describe the first practical and fully integrated cyberphysical error-recovery system that can be implemented in real time on a field-programmable gate array (FPGA). The hardware-assisted solution is based on an error dictionary containing the error-recovery plans for various anticipated errors. The dictionary is computed and stored in FPGA memory before the start of the biochemical experiment. Errors in droplet operations on the digital microfluidic platform are detected using capacitive sensors, the test outcome is interpreted by control hardware, and corresponding error-recovery plans are triggered in real-time. Experimental results are reported for a fabricated silicon device, and links to videos are provided for the first-ever experimental demonstration of real-time error recovery in cyberphysical digital-microfluidic biochips using a hardware-implemented dictionary.

Experimental demonstration of error recovery in an integrated cyberphysical digital-microfluidic platform

Recent advantages in flow-based microfluidic biochips have enabled the emergence of lab-on-a-chip devices for bimolecular recognition and point-of-care disease diagnostics. However, the adoption of flow-based biochips is hampered today by the lack of computer-aided design tools. Manual design procedures not only delay product development but they also inhibit the exploitation of the design complexity that is possible with current fabrication techniques. In this paper, we present the first practical problem formulation for automated control-layer design in flow-based microfluidic VLSI (mVLSI) biochips and propose a systematic approach for solving this problem. Our goal is to find an efficient routing solution for control-layer design with a minimum number of control pins. The pressure-propagation delay, an intrinsic physical phenomenon in mVLSI biochips, is minimized in order to reduce the response time for valves, decrease the pattern set-up time, and synchronize valve actuation. Two fabricated flow-based devices and five synthetic benchmarks are used to evaluate the proposed optimization method. Compared with manual control-layer design and a baseline approach, the proposed approach leads to fewer control pins, better timing behavior, and shorter channel length in the control layer.

Kai Hu

Papers

Testing of Flow-Based Microfluidic Biochips: Fault Modeling, Test Generation, and Experimental Demonstration

Fault detection, real-time error recovery, and experimental demonstration for digital microfluidic biochips

Control-Layer Routing and Control-Pin Minimization for Flow-Based Microfluidic Biochips

Experimental demonstration of error recovery in an integrated cyberphysical digital-microfluidic platform

Control-layer optimization for flow-based mVLSI microfluidic biochips