In　this　paper,　for　the　first　time,　the　deep　gated　recurrent　unit　(Deep　GRU)　is　used　as　a　new　macromodeling　approach　for　nonlinear　circuits.　Similar　to　Long　Short-Term　Memory　(LSTM),　the　GRU　has　gating　units　that　control　the　information　flow　and　makes　the　network　less　prone　to　the　vanishing　gradient　problem.　Having　a　smaller　number　of　gates　causes　GRU　to　have　fewer　parameters　compared　to　LSTM　leading　to　better　model　accuracy.　Using　the　gates　leads　gradient　formulations　to　have　additive　nature　which　helps　them　to　be　more　resistant　to　vanishing　and　consequently　learn　long　sequences　of　data.　The　proposed　macromodeling　method　is　capable　of　modeling　nonlinear　circuits　more　accurately　and　using　fewer　parameters　compared　to　the　conventional　LSTM　macromodeling　method.　To　further　improve　the　GRU　performance,　a　regularization　technique　called　Gaussian　dropout　is　applied　in　this　paper　on　deep　GRU　(GDGRU)　to　reduce　the　overfitting　problem　resulting　in　better　test　error.　Additionally,　the　models　obtained　from　the　proposed　techniques　are　remarkably　faster　than　the　original　transistor-level　models.　To　verify　the　superiority　of　the　proposed　method,　time-domain　modeling　of　three　nonlinear　circuits　is　provided.　For　these　circuits,　the　comparisons　of　the　accuracy　and　speed　between　the　conventional　recurrent　neural　network　(RNN),　the　LSTM,　and　the　proposed　macromodeling　methods　are　provided.