This　paper　introduces　an　advanced　model　for　microwave　components　using　the　Derivatives-Analysis-Assisted　Adjoint　Neural-Network　(DAANN)　technique.　The　proposed　technique　simultaneously　trains　input-output　behavior　of　the　microwave　component　and　electromagnetic　(EM)　simulation　derivatives　to　obtain　a　robust　parametric　model.　Exact　first　and　second-order　derivatives　of　general　multilayer　neural-network　structures　are　calculated　to　adjust　the　weights　of　DAANN,　ensuring　accurate　output　results.　New　formulations　have　been　derived　for　the　computation　of　second-order　derivatives.　The　DAANN　structure　provides　more　accurate　and　generalized　parametric　models　with　less　training　data　compared　to　existing　artificial　neural　network　(ANN)　structures.　This　technique　is　demonstrated　to　be　valid　through　an　example　of　a　four-pole　waveguide　filter.　©　2023　IEEE.