Artificial　intelligence　and　machine　learning　have　been　widely　used　to　replace　human　work.　Analog　integrated　circuit　design　needs　to　adjust　a　large　number　of　circuit　parameters　to　satisfy　the　balance　between　various　performance　metrics,　which　now　mostly　rely　on　the　experience　of　designers　and　sometimes　of　the　intuitions.　Machine　learning　has　demonstrated　the　potential　to　aid　the　design　of　analog　integrated　circuits　in　the　literature,　whilst　most　of　them　adopted　particle　swarm　intelligence　and　Bayesian　optimization.　However,　the　model　training　time　and　simulation　run　time　are　substantial　when　any　circuit　structure　modification　occurs.　In　this　paper,　Recurrent　Neural　Network　(RNN)　was　used　to　automatically　optimize　the　parameters　sizing　by　giving　requested　performance.　Training　data　sets　for　the　RNN　of　component　parameters　and　circuit　performance　were　simulated　using　Cadence　Spectre.　After　training　for　only　15　minutes,　RNN　learns　to　predict　parameters　by　inputting　gain,　bandwidth,　power　and　frequency,　which　can　make　critical　circuit　design　decision　significantly　faster.　The　reliability　and　applicability　of　the　algorithm　was　verified　through　the　parameter　prediction　of　integrated　operational　amplifier　and　VCO.　　©　2020　IEEE.