Error Recovery Method Based on Deep Reinforcement Learning for Fully Programmable Valve Array Biochips

Due to manufacturing defects, chip aging, and potential malicious attacks, unexpected errors may occur in the valves of Fully Programmable Valve Array (FPVA) biochips. To address this issue, an error recovery method based on Deep Reinforcement Learning (DRL) for FPVA biochips to handle valve-related unexpected errors is proposed, which involves designing specific error recovery operations for different error types, introducing a sequencing graph adjustment method to generate error recovery sequencing graph, and designing a resynthesis method to realize error recovery. The resynthesis method contains a priority-based scheduling adjustment, a DRL-based placement adjustment, and a DRL-based routing adjustment, which aims at updating the execution timetable for operations, component placements, and fluid transport paths. The model parameters are updated using a proximal policy optimization algorithm, continually learning from a large number of randomly simulated error scenarios, resulting in strong generalization performance. In comparison to existing work, the proposed method achieves lower probability of error recovery failure, shorter completion time of bioassay, and faster runtime.