We describe an exciting new application domain for deep reinforcement learning (RL): droplet routing on digital microfluidic biochips (DMFBs). A DMFB consists of a two-dimensional electrode array, and it manipulates droplets of liquid to automatically execute biochemical protocols for clinical chemistry. However, a major problem with DMFBs is that electrodes can degrade over time. The transportation of droplet transportation over these degraded electrodes can fail, thereby adversely impacting the integrity of the bioassay outcome. We demonstrated that the formulation of droplet transportation as an RL problem enables the training of deep neural network policies that can adapt to the underlying health conditions of electrodes and ensure reliable fluidic operations. We describe an RL-based droplet routing solution that can be used for various sizes of DMFBs. We highlight the reliable execution of an epigenetic bioassay with the RL droplet router on a fabricated DMFB. We show that the use of the RL approach on a simple micro-computer (Raspberry Pi 4) leads to acceptable performance for time-critical bioassays. We present a simulation environment based on the OpenAI Gym Interface for RL-guided droplet routing problems on DMFBs. We present results on our study of electrode degradation using fabricated DMFBs. The study supports the degradation model used in the simulator.

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

A digital microfluidic biochip (DMFB) enables the miniaturization of 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 control of droplets and seamlessly integrates microelectronics and microfluidics using CMOS technology and a TSMC fabrication process. To ensure that bioassays are carried out on MEDA biochips efficiently, high-level synthesis algorithms have recently been proposed. However, as in the case of conventional DMFBs, microelectrodes are likely to fail when they are heavily utilized, and previous methods fail to consider reliability issues. In this article, we first present a new microelectrode cell (MC) design such that the droplet-sensing operation can be enabled/disabled for individual MCs. Next, “partial update” and “partial sensing” operations are presented based on an IEEE Std. 1687 IJTAG network design. Finally, wear-leveling synthesis method is proposed to ensure uniform utilization of MCs on MEDA. A comprehensive set of simulation results demonstrate the effectiveness of the proposed hardware design and design automation methods.

The IEEE Std. 1687 facilitates flexible access to on-chip instruments through the JTAG test-access port. This flexibility enables the minimization of the overall access time (OAT), and a number of techniques have been proposed in the literature to achieve this goal. However, the OAT is still high for instruments that require a large amount of test data if this data is shifted through the scan chain serially. In order to further reduce the OAT, we present an efficient test-scheduling method that exploits broadcast and hardware parallelism for instrument access. A broadcast scheduling method is synergistically combined with three parallel IJTAG designs. We show that under different cost criteria, we can select the most efficient parallel IJTAG design such that the equivalent access time (EAT) is minimized. In addition, an interconnect fabric design and an integer-linear-programming method is used to balance the lengths of multiple scan chains. Two industry chip designs and three IJTAG benchmarks are used to evaluate the effectiveness of the proposed method.

A digital microfluidic biochip (DMFB) enables miniaturization of immunoassays, point-of-care clinical diagnostics, DNA sequencing, and other laboratory procedures in biochemistry. A recent generation of biochips uses a microelectrode-dot-array (MEDA) architecture, which provides fine-grained control of droplets and seamlessly integrates microelectronics and microfluidics using CMOS technology. To ensure that bioassays are carried out on MEDA biochips efficiently, high-level synthesis algorithms have recently been proposed. However, as in the case of conventional DMFBs, microelectrodes are likely to fail when they are heavily utilized, and previous methods fail to consider reliability issues. In this paper, we present the design of an IEEE Std. 1687 (IJTAG) network and a block-aware high-level synthesis method that can effectively alleviate reliability problems in MEDA biochips. A comprehensive set of simulation results demonstrate the effectiveness of the proposed method.

We present and investigate a novel application domain for deep reinforcement learning (RL): droplet routing on digital microfluidic biochips (DMFBs). A DMFB, composed of a two-dimensional electrode array, manipulates discrete fluid droplets to automatically execute biochemical protocols such as point-of-care clinical diagnosis. However, a major concern associated with the use of DMFBs is that electrodes in a biochip can degrade over time. Droplet-transportation operations associated with the degraded electrodes can fail, thereby compromising the integrity of the bioassay outcome. We show that casting droplet transportation as an RL problem enables the training of deep network policies to capture the underlying health conditions of electrodes and to provide reliable fluidic operations. We propose a new RL-based droplet-routing flow that can be used for various sizes of DMFBs, and demonstrate reliable execution of an epigenetic bioassay with the RL droplet router on a fabricated DMFB. To facilitate further research, we also present a simulation environment based on the OpenAI Gym Interface for RL-guided droplet-routing problems on DMFBs.

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.

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.

Sample preparation is an important application for the digital microfluidic biochips (DMFBs) platform, and many methods have been developed to reduce the time and reagent usage associated with on-chip sample preparation. However, errors in fluidic operations can result in the concentration of the resulting droplet being outside the calibration range. Current error-recovery methods have the drawback that they need the use of on-chip sensors and further re-execution time. In this paper, we present two dilution-chain structures that can generate a droplet with a desired concentration even if volume variations occur during droplet splitting. Experimental results show the effectiveness of the proposed method compared to previous methods.

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 paper, we present an effective fault- recovery solution based on the homogeneous structure of MEDA. Since the microelectrode cell (MCs) in a 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 (a.k.a. IJTAG) network to reduce the number of control signals need for the multiplexers, and to provide flexible sub-scan 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.

A digital microfluidic biochip (DMB) is an attractive platform for automating laboratory procedures in microbiology. However, a major problem associated with today's DMBs is the risk of cross-contamination due to undesirable fouling of the electrode surface, i.e., droplet materials stick to the surface. To overcome the above problem, a contactless liquid-handling biochip technology referred to as acoustofluidics has recently been proposed, and droplet manipulations on acoustofluidic biochips have also been experimentally demonstrated. In order to ensure robust fluidic operations and high confidence in the outcome of biochemical experiments, acoustofluidic biochips must be adequately tested before they are used for bioassay execution. This paper presents the first approach for testing of an acoustofluidic biochip that includes an array of interdigital transducers (IDTs). We first present structural test techniques to evaluate the pass/fail status of each IDT, and identify the type of fault if it fails. In order to ensure correct operation of functional units, e.g., mixers and routers, we also present functional test techniques to address fundamental acoustofluidic operations such as droplet transportation and droplet mixing. We evaluate the proposed test methods using experiments on fabricated acoustofluidic biochips.

Interposer-based 2.5D integrated circuits (ICs) are being increasingly adopted in the semiconductor industry for FPGAs and GPUs. However, the cost of testing is still a major concern for 2.5D ICs because if a faulty die is detected after it is bonded to the interposer, the entire 2.5D assembly has to be discarded. We consider 2.5D integration based on the use of microsprings for attaching dies to the interposer. A key advantage of microsprings is that they allow the 2.5D assembly to be reworkable. If a faulty die is detected during post-bond testing, we can replace the faulty die with a fault-free one. In order to quantify the benefit of the reworkable 2.5D assembly, we present a test-flow selection method for 2.5D ICs with microsprings. We compare the test cost of microspring-based integration with a baseline of test flows for microbump-only integration, with respect to some key parameters such as pre-bond test cost, fault coverage of tests, and microspring cost. For a large number of dies and a relatively low die yield, microsprings provide significant benefits over the baseline.

The IEEE Std. 1687 facilitates flexible access to on-chip instruments through the JTAG test-access port. This flexibility enables the minimization of the overall access time (OAT), and a number of techniques have been proposed in the literature to achieve this goal. However, the OAT is still high for instruments that require a large amount of test data if this data is shifted through the scan chain serially. In order to further reduce the OAT, we present an efficient test-scheduling method that exploits broadcast and hardware parallelism for instrument access. A broadcast scheduling method is synergistically combined with three parallel IJTAG designs. We show that under different cost criteria, we can select the most efficient parallel IJTAG design such that the equivalent access time (EAT) is minimized. Two industry chip designs and three IJTAG benchmarks are used to evaluate the effectiveness of the proposed method.

The IEEE Std. 1687 enables flexible access to on-chip instruments through the JTAG test-access port. This flexibility enables the minimization of the overall access time (OAT), and a number of techniques have been proposed to achieve this goal. However, these techniques do not utilize the broadcast feature in the 1687 network. In order to further reduce the OAT, we present an efficient test-scheduling method that exploits the broadcast feature for instrument access. Two optimization solutions are then proposed - the first solution minimizes the OAT without the retargeting time, while the second one reorders the configurations so as to minimize the overall retargeting time among configurations. Three industry test cases are used to evaluate the effectiveness of the proposed method.

Digital microfluidic biochips (DMFBs) are being increasingly used in biochemistry labs for automating bioassays. However, traditional DMFBs suffer from some key shortcomings: 1) inability to vary droplet volume in a flexible manner; 2) difficulty of integrating on-chip sensors; 3) the need for special fabrication processes. To overcome these problems, DMFBs based on micro-electrode-dot-array (MEDA) have recently be-en proposed. However, errors are likely to occur on a MEDA DMFB due to chip defects and the unpredictability inherent to biochemical experiments. We present fine-grained error-recovery solutions for MEDA by exploiting real-time sensing and advanced MEDA-specific droplet operations. The proposed methods rely on adaptive droplet-aliquot operations and predictive analysis of mixing. Experimental results on three representative benchmarks demonstrate the efficiency of the proposed error-recovery strategy.

A digital microfluidic biochip (DMFB) is an attractive technology platform for various biomedical applications. However, a conventional DMFB is limited by: (i) the number of electrical connections that can be practically realized, (ii) constraints on droplet size and volume, and (iii) the need for special fabrication processes and the associated reliability/yield concerns. To overcome the above challenges, DMFBs based on a micro-electrode-dot-array (MEDA) architecture have been proposed and fabricated recently. Error recovery is of key interest for MEDA biochips due to the need for system reliability. Errors are likely to occur during droplet manipulation due to defects, chip degradation, and the uncertainty inherent in biochemical experiments. In this paper, we first formalize error-recovery objectives, and then synthesize optimal error-recovery protocols using a model based on Stochastic Multiplayer Games (SMGs). We also present a global error-recovery technique that can update the schedule of fluidic operations in an adaptive manner. Using three representative real-life bioassays, we show that the proposed approach can effectively reduce the bioassay completion time and increase the probability of success for error recovery.