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.

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.

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.

Microfluidics-based biochips enable the precise control of nanoliter volumes of biochemical samples and reagents. They combine electronics with biology, and they integrate various bioassay operations, such as sample preparation, analysis, separation, and detection. Compared to conventional laboratory procedures, which are cumbersome and expensive, miniaturized biochips offer the advantages of higher sensitivity, lower cost due to smaller sample and reagent volumes, system integration, and less likelihood of human error. This paper first describes the droplet-based “digital” microfluidic technology platform and emerging applications. The physical principles underlying droplet actuation are next described. Finally, the paper presents computer-aided design tools for simulation, synthesis and chip optimization. These tools target modeling and simulation, scheduling, module placement, droplet routing, pin-constrained chip design, and testing.

Microfluidics-based biochips are soon expected to revolutionize clinical diagnosis, deoxyribonucleic acid (DNA) sequencing, and other laboratory procedures involving molecular biology. In contrast to continuous-flow systems that rely on permanently etched microchannels, micropumps, and microvalves, digital microfluidics offers a scalable system architecture and dynamic reconfigurability; groups of unit cells in a microfluidics array can be reconfigured to change their functionality during the concurrent execution of a set of bioassays. As more bioassays are executed concurrently on a biochip, system integration and design complexity are expected to increase dramatically. This paper presents an overview of an integrated system-level design methodology that attempts to address key issues in the synthesis, testing and reconfiguration of digital microfluidics-based biochips. Different actuation mechanisms for microfluidics-based biochips, and associated design-automation trends and challenges are also discussed. The proposed top-down design-automation approach is expected to relieve biochip users from the burden of manual optimization of bioassays, time-consuming hardware design, and costly testing and maintenance procedures, and it will facilitate the integration of fluidic components with a microelectronic component in next-generation systems-on-chips (SOCs).

Composite microsystems that incorporate microelectromechanical and microelectrofluidic devices are emerging as the next generation of system-on-a-chip (SOC). We present a performance comparison between two types of microelectrofluidic systems (MEFS): continuous-flow systems and droplet-based systems. The comparison is based on a specific microelectrofluidic application-a polymerase chain reaction (PCR) system. The behavioral modeling, simulation, and performance evaluation are based on a SystemC design environment. The performance comparison includes the system throughput, system-correction capacity, system-processing capacity, and system-design complexity. By using our system-performance evaluation environment, we demonstrated that the droplet-based MEFS provides higher performance, as well as lower design and integration complexity.

Composite microsystems that integrate mechanical and fluidic components with electronics are emerging as the next generation of system-on-a-chip. Custom microsystems are expensive, inflexible, and unsuitable for high-volume production. The authors address this problem by leveraging hardware/software codesign principles to design reconfigurable composite microsystems. They partition the system design parameters into nonreconfigurable and reconfigurable categories. In this way, operational flexibility is enhanced and the microsystems are designed for a wider range of application. In addition, the Taguchi robust design method is used to make the system robust, and response surface methodologies are used to explore the widest performance range for the system. A case study is presented for a microvalve, which serves as a representative microelectrofluidic device.

We present an architectural design and optimization methodology for performing biochemical reactions using two-dimensional (2-D) electrowetting arrays. We define a set of basic microfluidic operations and leverage electronic design automation principles for system partitioning, resource allocation, and operation scheduling. Fluidic operations are carried out through the electrostatic configuration of a set of grid points. While concurrency is desirable to minimize processing time, the size of the 2-D array limits the number of concurrent operations of any type. Furthermore, functional dependencies between the operations also limit concurrency. We use integer linear programming to minimize the processing time by automatically extracting parallelism from a biochemical assay. As a case study, we apply our optimization method to the polymerase chain reaction, which is an important step in many lab-on-a-chip biochemical applications.

Ion implantation of dopant impurities to form p-n junctions and other doped regions in silicon transistors has evolved from an experimental curiosity in solid-state physics to become a dominant technology in today's integrated circuit manufacturing. This paper traces the key inventions and early developments in ion beam doping concepts from the early 1950's through the 1970's as they were applied to the development of metal-oxide-semiconductor (MOS) and bipolar transistors. Early on, scientists were fascinated with possible applications of radiation damage resulting from ion bombardment of semiconductors for building novel devices with room-temperature processing or, at most, maximum heating temperatures of a few hundred degrees Celsius. As a result, the early work initially overlooked the real potential of the technology for providing a highly controlled means of substitutional impurity doping in combination with high temperature annealing above 800/spl deg/C. Subsequently, a number of key researchers contributed to the knowledge base surrounding the penetration of energetic ions into semiconductors. However, the true potential of ion implantation would be realized by more device-oriented engineers driven by design and manufacturing issues who looked for ways of applying and integrating ion implantation with conventional transistor manufacturing methods. Ion implantation allowed for good lateral and vertical junction position control, which were critical elements as both device dimensions and patterned feature tolerances were reduced. Ion implantation, high-temperature annealing, and self-aligned silicon gate technology fueled new process technologies in MOS devices such as arsenic source/drain doping, the ability to tailor threshold voltages in both enhancement and depletion transistors, and lightly doped drain extensions. In bipolar technology, ion implantation would make possible tight control of base-region doping and reduction of parasitic resistances, and would enable the use of arsenic emitters for high-frequency applications of discrete transistors as well as advanced, high-speed integrated circuits.

Two-dimensional (2-D) effects are becoming increasingly important in the diffusion of impurities in submicrometer silicon devices. The authors describe a 2-D process simulator, PREDICT2, that handles implant damage effects, annealing, and lateral diffusion. PREDICT2 simulates the diffusion of impurities in silicon by using phenomenological diffusion coefficients. The phenomenological models are verified by comparing simulated and experimental results. This approach is compared with that of point-defect-based simulators. The experimental technique for measuring impurity profiles in two dimensions is outlined. The method for generating diffusion models is illustrated by examining 2-D phosphorous diffusion. Numerical simulations and experimental measurements are compared. The numerical methods used in the simulator are described, and directions for future work are suggested.>

It is argued that the cost of manufacturing submicron, ultra-large-scale integration (ULSI) integrated circuits (ICs) (which is scaling upward at least in inverse proportion to the downward scaling of device feature sizes) is driven by a parameter budget crisis associated with the technological and complexity limits of IC design and fabrication. This budget crisis is defined for technologies associated with patterning, mechanical control, thermal treatments, particulates and contamination, defects, electrical parameters, and manufacturing, R&D priorities for silicon ULSI are identified and quantified, and the potential impact of some recent research results is discussed. It is pointed out that the R&D agenda to deal with the ULSI manufacturing budget crisis is enormous, and the semiconductor industry cannot perform all of it. Since the time for transferring new developments into production tools and technologies is five to seven years, it is concluded that critical funding decisions need to be made now for ULSI manufacturing science breakthroughs by the turn of the century.>