The　Wiener-type　dynamic　neural　network　(DNN)　approach　can　be　used　for　nonlinear　device　modeling.　The　analytical　formulation　of　Wiener-type　DNN　structure　consists　of　a　cascade　of　a　simplified　linear　dynamic　part　and　a　nonlinear　static　part　based　on　a　Wiener　system　formulation.　The　Wiener-type　DNN　model　can　be　trained　to　be　accurate　relative　to　device　data.　Furthermore,　the　Wiener-type　DNN　provides　enhanced　convergence　properties　over　existing　neural　network　approaches　such　as　time　delay　neural　network　(TDNN)　and　TDNN　with　extrapolation.　Modeling　of　GaAs　metal-semiconductor-field-effect　transistor　(MESFET)　is　presented.　In　this　paper,　we　address　the　use　of　Wiener-type　DNN　model　in　harmonic　balance　simulations　which　demonstrate　that　the　Wiener-type　DNN　is　a　robust　approach　for　modeling　microwave　devices.　It　is　useful　for　systematic　and　automated　update　of　nonlinear　device　model　library　for　existing　circuit　simulators.