This　paper　introduces　a　novel　decomposition　technique　for　parametric　modeling　microwave　components　whose　geometric　variable　values　vary　over　a　wide　range.　This　technique　includes　generating　parallel　data,　training　sub-models　in　parallel,　and　modifying　sub-models　in　parallel.　A　second-derivative-based　algorithm　is　used　to　divide　a　large　range　of　geometric　variables　into　a　series　of　sub-ranges,　leading　to　the　decomposition　of　a　highly　nonlinear　region　into　numerous　sub-regions,　each　of　which　is　trained　to　obtain　an　artificial　neural　network　(ANN)　model　with　a　simple　structure.　However,　the　problem　of　complex　multidimensional　discontinuities　occurs　in　the　process　of　connecting　all　the　sub-models　into　an　overall　model.　Therefore,　a　solution　to　this　problem　is　proposed　by　introducing　a　sub-model　modification　process,　so　that　this　overall　model　can　be　used　for　design　optimization.　The　decomposition　technique　can　enhance　the　accuracy　with　short　model-development　time　compared　with　the　methods　that　train　the　overall　wide　range　directly　into　a　model.　The　proposed　technique　is　demonstrated　to　be　valid　through　an　example　of　an　Inter-Digital　Bandpass　Filter.　©　2023　IEEE.