Digital microfluidic biochips (DMFBs) can effectively reduce the cost of biochemical analysis and improve experimental efficiency, as they are easy to carry, use fewer reagent samples and have high precision. Paper-Based Digital Microfluidic Biochips (PB-DMFBs) are a branch of microfluidic biochips. This technology prints ink containing carbon nanotubes on special paper to form electrodes and control wire, so the manufacturing cost and time required are far less than the traditional digital microfluidic chip, in which droplets move between two control layers. However, the chip-level synthesis of PB-DMFBs becomes more challenging because all circuits of PBDMFBs are printed on a single paper layer. Furthermore, current PB-DMFB designs must address various issues, including fabrication cost, reliability, and safety. Therefore, a more flexible method for the chip-level synthesis of PB-DMFBs is needed. In this paper, we propose a chip-level synthesis method of PB-DMFBs based on reinforcement learning. Double Deep Q-learning Networks (Double DQN) are suitable for agents to select actions and estimate actions, and then obtain optimized comprehensive results. Experimental results demonstrate that the proposed method is not only effective and efficient for chip-level synthesis, but also scalable to applications with high reliability and safety requirements.

Paper-based digital microfluidic biochip (PB-DMFB) technology provides a promising solution to many biochemical applications. However, the PB-DMFB manufacturing process may suffer from potential security threats. For example, a Trojan insertion attack may affect the functionality of PB-DMFBs. To ensure the correct functionality of PB-DMFBs, we propose a watermarking scheme to hide information in the PB-DMFB layout, which allows users to check design integrity and authenticate the source of the PB-DMFB design. As a result, the proposed method serves as a countermeasure against Trojan insertion attacks in addition to proof of authorship.

Paper-based digital microfluidic biochips (PB-DMFBs) have emerged as the most promising solution to biochemical applications in resource-limited regions. However, like silicon chips, the reliability of PB-DMFBs is affected by physical defects. Even worse, since electrodes, conductive wires, and droplet routings are entangled on the same layer, multiple faults may occur simultaneously. Such faults not only cause waste of samples and human resource but also affect the correctness of the diagnostics. In this paper, we propose a reliability scheme with emphasis on design-for-reliability (DfR) and probability-based fault tolerance to ensure the correct functionality of PB-DMFBs with multiple faults.

Fully programmable valve arrays (FPVAs) have emerged as a new technology commonly used for biochemical applications. FPVAs have the programmability to perform any bioassay as long as users obtain the fluidic-level synthesis results to configure the fluid loading. Users can purchase a bioassay and the corresponding synthesis result from any bioassay provider. However, the distributed design stages are vulnerable to security threats. Trojans are the most critical threats since they can be inserted in any design stage. Even worse, Trojans would not result in a significant deviation from the original synthesis results, while they can affect the bioassay execution dramatically. In this paper, we propose the six Trojan models for FPVAs and a systematic method for Trojan insertion. In the experiments, we insert Trojans into ten test cases. Most of the Trojan-inserted synthesis results are similar to Trojan-free ones in terms of the efficiency metrics. In other words, the experimental results show that the proposed Trojans for FPVAs are stealthy.

To achieve wafer testing with high precision, it is necessary to thoroughly clean the probing needles. Currently, the level of cleanliness is determined according to prior experience and the frequency of die failures. The current cleaning process is inefficient since there is no mechanism to identify embedded and bonded debris (foreign material) prior to the test, which may result in yield loss. In addition, over cleaning reduces a needle's life. To address this problem, we propose a framework based on real-time images and die contact resistance. The proposed framework employs image processing and supervised machine learning techniques to develop a recognition framework for detecting sudden foreign material buildups and monitoring needle degradation over time.

Paper-based digital microfluidic biochip (PB-DMFB) technology is one of the most promising solutions in biochemical applications due to the paper substrate. The paper substrate makes PB-DMFBs more portable, cost-effective, and less dependent on manufacturing equipment. However, the single-layer paper substrate, which entangles electrodes, conductive wires, and droplet routing in the same layer, raises challenges to chip-level synthesis of PB-DMFBs. Furthermore, current design automation tools have to address various design issues including manufacturing cost, reliability, and security. Therefore, a more flexible chip-level synthesis method is necessary. In this paper, we propose the first reinforcement learning based chip-level synthesis for PB-DMFBs. Double deep Q-learning networks are adapted for the agent to select and estimate actions, and then we obtain the optimized synthesis results. Experimental results show that the proposed method is not only effective and efficient for chip-level synthesis but also scalable to reliability and security-oriented schemes.

Paper-based digital microfluidic biochip (PB-DMFB) technology provides a promising solution to many biochemical applications. However, PB-DMFB manufacturing process may suffer from potential security threats. For example, both Trojan insertion and man-in-the-middle attack may affect the functionality of PB-DMFBs. To ensure the correct functionality of PB-DMFBs, we propose a watermarking scheme to hides information in the PB-DMFB layout, which allows users to check design integrity and authenticate the source of the PB-DMFB design. As a result, it serves as a first countermeasure against both Trojan insertion and man-in-the-middle attacks for PB-DMFBs.

Paper-based digital microfluidic biochips (PB-DMFBs) provide a promising solution for microfluidic bioassays. Due to the low-cost substrate material and low demand for complicated manufacturing equipment, PB-DMFBs can be fabricated without foundry. On the flip side, convenience of fabrication allows PB-DMFBs to be fabricated everywhere, which makes it is difficult to manage production and distribution of IP (bioassays). As a result, PB-DMFBs are vulnerable to security threats. IP and its creator, the biocoders, may suffer from infringement. To ensure IP protection, in this paper, we proposed the first Digital Rights Management (DRM) scheme to protect IPs of PB-DMFBs from security threats. A chip-level synthesis algorithm is also presented to realize not only complex biochemical operations but also the demand of DRM.