In　the　computer-aided　design　(CAD)　area,　the　recurrent　neural　network　(RNN)　has　shown　notable　functionality　in　generating　fast　and　high-performance　models　rather　than　the　models　in　simulation　tools.　Predicting　time　sequences　is　a　pervasive　and　challenging　problem　that　may　require　identifying　the　dependencies　between　sequences　that　RNN　is　capable　of　performing.　Despite　all　its　features,　conventional　RNN　still　faces　challenges　such　as　limited　accuracy　and　a　large　number　of　parameters.　Therefore,　we　propose　new　macromodeling　methods　for　nonlinear　circuits　called　the　Clockwork-RNN　(CWRNN)　and　its　hybrid　version　which　is　a　more　powerful　but　simpler　implementation　of　a　conventional　RNN　architecture　with　relatively　little　model　complexity.　In　addition,　CWRNN　inherently　models　complex　dependencies　without　the　need　for　a　large　number　of　parameters.　As　a　result,　the　computational　cost　is　less　than　conventional　RNN.　Moreover,　understanding　and　implementing　the　CWRNN　is　relatively　simple　and　provides　great　flexibility　in　architectural　configuration　by　introducing　modules　with　several　clock　rates　of　exponents　of　2.　In　addition　to　the　above　new　modeling　technique,　we　proposed　the　Hybrid-Module　CWRNN　as　another　new　modeling　method　that　utilizes　modules　of　various　exponents　of　different　numbers　resulting　in　further　accuracy　improvement　of　the　CWRNN.　Furthermore,　the　models　obtained　from　the　proposed　techniques　required　much　smaller　simulation　times　compared　to　the　current　models　used　in　simulation　tools.　Three　nonlinear　high-frequency　examples　have　been　utilized　to　verify　the　benefits　of　the　proposed　modeling　methods.