This　paper　presents　an　enhanced　automated　model　generation　(AMG)　algorithm　using　neural　networks　(NNs).　AMG　performs　automated　sampling　of　training/testing　data　and　automated　NN　structure　adaptation　to　obtain　a　compact　NN　model　of　user-required　accuracy　with　suitable　amount　of　data.　The　proposed　enhanced　AMG　performs　data　sampling　in　a　stage-wise　manner.　In　each　stage,　instead　of　sampling　along　all　dimensions　of　the　input　space,　the　proposed　AMG　distinguishes　the　input　dimension　which　influences　the　nonlinearity　of　the　model　behavior　most　in　current　stage,　then　generates　additional　training　samples　along　this　input　dimension.　Compared　to　existing　AMG,　the　proposed　algorithm　can　reduce　the　amount　of　data　needed　for　NN　modeling,　especially　in　the　case　of　multidimensional　parametric　modeling　of　microwave　devices.　Examples　including　automated　modeling　of　MOSFET　and　bandpass　filter　are　presented　to　demonstrate　the　validity　of　the　proposed　AMG.　©　2020　IEEE.