Routability　estimation　identifies　potentially　congested　areas　in　advance　to　achieve　high-quality　routing　solutions.　To　improve　the　routing　quality,　this　paper　presents　a　deep　learning-based　congestion　estimation　algorithm　that　applies　the　estimation　to　a　global　router.　Unlike　existing　methods　based　on　traditional　compressed　2D　features　for　model　training　and　prediction,　our　algorithm　extracts　appropriate　3D　features　from　the　placed　netlists.　Furthermore,　an　improved　RUDY　(Rectangular　Uniform　wire　DensitY)　method　is　developed　to　estimate　3D　routing　demands.　Besides,　we　develop　a　congestion　estimator　by　employing　a　U-net　model　to　generate　a　congestion　heatmap,　which　is　predicted　before　global　routing　and　serves　to　guide　the　initial　pattern　routing　of　a　global　router　to　reduce　unexpected　overflows.　Experimental　results　show　that　the　Pearson　Correlation　Coefficient　(PCC)　between　actual　and　our　predicted　congestion　is　high　at　about　0.848　on　average,　significantly　higher　than　the　counterpart　by　21.14%.　The　results　also　show　that　our　guided　routing　can　reduce　the　respective　routing　overflows,　wirelength,　and　via　count　by　averagely　6.05%,　0.02%,　and　1.18%,　with　only　24%　runtime　overheads,　compared　with　the　state-of-the-art　CUGR　global　router　that　can　balance　routing　quality　and　efficiency　very　well.　In　particular,　our　work　provides　a　new　generic　machine　learning　model　for　not　only　routing　congestion　estimation　demonstrated　in　this　paper,　but　also　general　layout　optimization　problems.　©　2022　IEEE.