With　the　advancement　of　electronic　design　automation,　continuous-flow　microfluidic　biochips　have　become　one　of　the　most　promising　platforms　for　biochemical　experiments.　This　chip　manipulates　fluid　samples　in　milliliters　or　nanoliters　by　utilizing　internal　microvalves　and　microchannels,　and　thus　automatically　performs　basic　biochemical　experiments,　such　as　mixing　and　detection.　To　achieve　the　correct　bioassay　function,　the　microvalves　deployed　inside　the　chip　are　usually　managed　by　a　multiplexer-based　control　logic,　and　valves　receive　control　signals　from　a　core　input　through　the　control　channel　for　accurate　switching.　Since　biochemical　reactions　typically　require　high　sensitivity,　the　length　of　control　paths　connecting　each　valve　needs　to　be　reduced　to　ensure　immediate　signal　propagation,　and　thus　to　reduce　the　signal　propagation　delay.　In　addition,　to　reduce　the　fabrication　cost　of　chips,　a　vital　issue　to　be　addressed　in　the　logic　architecture　design　is　how　to　effectively　reduce　the　total　channel　length　within　the　control　logic.　To　address　the　above　issues,　we　propose　a　deep　reinforcement　learning-based　control　logic　routing　algorithm　to　minimize　the　signal　propagation　delay　and　total　control　channel　length,　thereby　automatically　constructing　an　efficient　control　channel　network.　The　algorithm　employs　the　dueling　deep　Q-network　architecture　as　the　agent　of　the　deep　reinforcement　learning　framework　to　evaluate　the　tradeoff　between　signal　propagation　delay　and　total　channel　length.　Besides,　the　diagonal　channel　routing　is　implemented　for　the　first　time　for　control　logic,　thus　fundamentally　improving　the　efficiency　of　valve　switching　operations　and　reducing　the　fabrication　cost　of　the　chip.　The　experimental　results　demonstrate　that　the　proposed　algorithm　can　effectively　construct　a　high-performance　and　low-cost　control　logic　architecture.　©　2025　Science　Press.　All　rights　reserved.