A Routing-Based Repair Method for Digital Microfluidic Biochips Based on an Improved Dijkstra and Improved Particle Swarm Optimization Algorithm

Abstract

Digital microfluidic biochips (DMFBs) are attractive instruments for obtaining modern molecular biology and chemical measurements. Due to the increasingly complex measurements carried out on a DMFB, such chips are more prone to failure. To compensate for the shortcomings of the module-based DMFB, this paper proposes a routing-based fault repair method. The routing-based synthesis methodology ensures a much higher chip utilization factor by removing the virtual modules on the chip, as well as removing the extra electrodes needed as guard cells. In this paper, the routing problem is identified as a dynamic path-planning problem and mixed path design problem under certain constraints, and an improved Dijkstra and improved particle swarm optimization (ID-IPSO) algorithm is proposed. By introducing a cost function into the Dijkstra algorithm, the path-planning problem under dynamic obstacles is solved, and the problem of mixed path design is solved by redefining the position and velocity vectors of the particle swarm optimization. The ID-IPSO routing-based fault repair method is applied to a multibody fluid detection experiment. The proposed design method has a stronger optimization ability than the greedy algorithm. The algorithm is applied to 8×9, 8×8, and 7×8 fault-free chips. The proposed ID-IPSO routing-based chip design method saves 13.9%, 14.3%, and 14.5% of the experiment completion time compared with the greedy algorithm. Compared with a modular fault repair method based on the genetic algorithm, the ID-IPSO routing-based fault repair method has greater advantages and can save 39.3% of the completion time on average in the completion of complex experiments. When the ratio of faulty electrodes is less than 12% and 23%, the modular and ID-IPSO routing-based fault repair methods, respectively, can guarantee a 100% failure repair rate. The utilization rate of the electrodes is 18% higher than that of the modular method, and the average electrode usage time is 17%. Therefore, the ID-IPSO routing-based fault repair method can accommodate more faulty electrodes for the same fault repair rate; the experiment completion time is shorter, the average number of electrodes is lower, and the security performance is better.

A design method based on Bayesian decision for routing-based digital microfluidic biochips

“…Therefore, in this paper, we checked the cell utilization count with respect to the module-based method 49 and the improved Dijkstra and improved particle swarm optimization (ID- IPSO ) routing techniques. 50 To verify the influence of the electrode actuation times on the chip reliability of our method, a 10 × 10 chip design is completed by the BBD method on a multiple body fluid detection experiment. To improve the reliability of the digital microfluidic chip, we simultaneously optimize the utilization rate and reliability of the electrode during chip design.…”

“…To analyze the optimization effect of the BBD design method on the electrode driving time in chip design of different sizes, the proposed method is tested by varying the chip size from 7 × 7 to 16 × 16 for PCR experiments. We check the drive times with respect to the module-based method, 49 the ID- IPSO method, 50 and our proposed method in this paper. The results are shown in Fig.…”

Structural Optimization Design of Microfluidic Chips Based on Fast Sequence Pair Algorithm

“…References [ 21 , 22 ] proposed an arbitrary angle routing algorithm. In response to storage issues, References [ 23 , 24 ] proposed a distributed channel storage method. References [ 25 , 26 , 27 ] proposed a weighted sum algorithm for the total length of microchannels.…”

A Reconfigurable Modular Vehicle Control Strategy Based on an Improved Artificial Potential Field

“…Path planning is finding the shortest possible path between two points [3]. There exist many mature algorithms for path planning, such as Breadth First Search (BFS) and Depth First Search (DFS), and further optimization algorithms include the Dijkstra algorithm [4], A* algorithm [5], Bellman-Ford algorithm [6], etc., which traverse the map structure based on the path weight and iteratively calculate the path cost during the search, followed by backtracking to obtain an optimal solution when the calculation is completed. The artificial potential field method abstracts the motion of a robot in the surrounding environment into motion in an artificial gravitational field [7].…”