Adaptive Control-Logic Routing for Fully Programmable Valve Array Biochips Using Deep Reinforcement Learning

Abstract

With the increasing integration level of flow-based microfluidics, fully programmable valve arrays (FPVAs) have emerged as the next generation of microfluidic devices. Mi-crovalves 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 states switching. The critical valves that suffer from asynchronous actuation leading to chip malfunctions, however, need to be switched simultaneously in a specific bioassay. As a result, the channel lengths from the core input to these valves are required to be equal or similar, which poses a 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 signals propagation. Meanwhile, the timing skews among synchronized valves and the total length of control channels can be minimized, thus generating an optimized control logic with excellent timing behavior. Simulation results on multiple benchmarks demonstrate the effectiveness of the proposed routing flow.