This　letter　proposes　an　advanced　automated　model　generation　(AMG)　of　microwave　components　using　an　adjoint　artificial　neural　network　(ANN)　and　electromagnetic　(EM)　sensitivity　analysis.　EM　sensitivities　are　integrated　into　the　AMG　process　for　the　first　time　to　speed　up　the　automated　ANN　model　development.　We　propose　a　novel　adaptive　sampling　algorithm,　combining　EM　sensitivities　and　interpolation　techniques　to　dynamically　determine　the　optimal　sampling　scheme.　This　ensures　obtaining　the　most　accurate　ANN　with　minimal　data.　We　also　propose　a　new　adjoint　ANN　training　method　with　EM　sensitivities　to　automatically　determine　the　suitable　ANN　structure.　By　utilizing　both　EM　data　and　EM　sensitivities,　the　modeling　efficiency　of　the　proposed　AMG　is　effectively　improved　compared　to　existing　AMGs.　Two　microwave　modeling　examples　are　provided　to　demonstrate　the　proposed　algorithm.