A design method based on Bayesian decision for routing-based digital microfluidic biochips. Issue 6 (23rd February 2022) - Shi, Jinlong; Fu, Ping; Zheng, Wenbin
Abstract : A design method based on Bayesian decision-making for consider the reliability of digital microfluidic chip was proposed. Simulated results showed the method can achieve shorter assay completion time, high electrode usage rate than other methods. Abstract : With the continuous application and development of the digital microfluidic technology in various fields, many researchers have studied the design of digital microfluidic chips. Module-based chip design methods greatly simplify the design process but waste resources, including through the inadequate use of electrodes within the module and guard cells. To address this problem, a routing-based synthesis method based on a digital microfluidic biochip (DMFB) platform is presented. Routing-based DMFBs ensure a much higher chip utilization factor by removing the virtual modules on the chip and the extra electrodes needed as guard cells. Many previous works focused only on the problems of synthesis completion times, bioassay completion times, and electrode utilization rates. However, the reliability of chips has not been fully studied, and this factor is extremely important because faulty chips affect the test results. Thus, the influence of chip reliability should be fully considered. This paper proposes a design method based on Bayesian decision-making (BBD) for routing-based DMFBs that can fully consider the reliability of chips during the DMFB design process. Simulated experimental results showed that the method can address the reliability problems of chips. The efficiency and convergence performance of the algorithm were very good. The proposed method can achieve an average assay completion time that is shorter than those of the moduleless synthesis (MLS) and modified-MLS (MMLS) methods. The electrode usage rate of the proposed method is better than that of the module-based and improved Dijkstra and improved particle swarm optimization (ID- IPSO ) methods.