As　timing　delay　becomes　a　critical　issue　in　chip　performance,　there　is　a　burning　desire　for　IC　design　under　smart　manufacturing　to　optimize　the　delay.　As　the　best　connection　model　for　multi-terminal　nets,　the　wirelength　and　the　maximum　source-to-sink　pathlength　of　the　Steiner　minimum　tree　are　the　decisive　factors　of　timing　delay　for　routing.　In　addition,　considering　that　X-routing　can　get　the　utmost　out　of　routing　resources,　this　article　proposes　a　Timing-Driven　X-routing　Steiner　Minimum　Tree　(TD-XSMT)　algorithm　based　on　two-stage　competitive　particle　swarm　optimization.　This　work　utilizes　the　multi-objective　particle　swarm　optimization　algorithm　and　redesigns　its　framework,　thus　improving　its　performance.　First,　a　two-stage　learning　strategy　is　presented,　which　balances　the　exploration　and　exploitation　capabilities　of　the　particle　by　learning　edge　structures　and　pseudo-Steiner　point　choices.　Especially　in　the　second　stage,　a　hybrid　crossover　strategy　is　designed　to　guarantee　convergence　quality.　Second,　the　competition　mechanism　is　adopted　to　select　particle　learning　objects　and　enhance　diversity.　Finally,　according　to　the　characteristics　of　the　discrete　TD-XSMT　problem,　the　mutation　and　crossover　operators　of　the　genetic　algorithm　are　used　to　effectively　discretize　the　proposed　algorithm.　Experimental　results　reveal　that　TSCPSO-TD-XSMT　can　obtain　a　smooth　trade-off　between　wirelength　and　maximum　source-to-sink　pathlength,　and　achieve　distinguished　timing　delay　optimization.　©　2022　Association　for　Computing　Machinery.