Adaptive Control-Logic Routing with Length Matching and Fault Tolerance for FPVA Biochips Using Deep Reinforcement Learning

With the increasing integration level of flow-based microfluidics, fully programmable valve arrays (FPVAs) have emerged as the next generation of flow-based microfluidic devices. Microvalves in an FPVA are typically managed by a control logic, where valves are connected to a core input via control channels to receive control signals that guide their state switchings. When executing bioassays using an FPVA, however, some valves need to be switched synchronously at different time points, so that both fluid transportation and biochemical operations can be executed correctly. Consequently, the channel lengths from the core input to these valves must be equal, which poses a big challenge to the channel routing of the control logic. To solve this problem, we propose a deep reinforcement learning-based adaptive routing flow for the control logic of FPVAs. With the proposed routing flow, an efficient control-channel network can be automatically constructed to realize accurate control signal propagation. Meanwhile, timing skews among synchronized valves and the total length of control channels are minimized simultaneously, thus generating an optimized control logic with excellent timing performance. Furthermore, by introducing backups of identified critical valves and flexible routing of backup paths without restrictions on control valve locations, a novel fault-tolerant design method considering length matching is implemented to efficiently improve the reliability of the control logic. Simulation results on multiple benchmarks demonstrate that the proposed routing flow leads to control logics with accurate valve synchronization, low cost, and high reliability.