This　article　proposes　a　novel　macromodeling　method　for　high-frequency　nonlinear　circuits,　utilizing　an　attention-based　deep　recurrent　neural　network　(ATDRNN).　This　method　leverages　the　attention　mechanism　within　the　RNN,　comparing　each　time　step　with　other　time　steps　to　determine　their　similarities.　It　then　applies　some　coefficients　as　weights　to　the　features　of　each　time　step　based　on　these　similarities,　enhancing　the　RNN＇s　ability　to　focus　on　more　informative　features.　Consequently,　this　approach　allows　for　more　accurate　modeling　of　nonlinear　circuits.　Additionally,　having　comprehensive　signal　information　and　similarities　between　various　time　steps　mitigates　the　vanishing　gradient　problem　commonly　faced　by　RNNs.　The　models　derived　from　this　method　not　only　exhibit　superior　accuracy　compared　to　the　conventional　RNNs,　but　also　run　much　faster　than　existing　transistor-level　models　in　circuit　simulators.　The　effectiveness　of　the　proposed　method　is　demonstrated　by　modeling　two　nonlinear　circuits,　namely　2-coupled　and　3-coupled　line　high-speed　interconnects　driven　by　multi-stage　buffers.