This　article　presents　an　overview　of　artificial　neural　network　(ANN)　techniques　for　a　microwave　computer-aided　design　(CAD).　ANN-based　techniques　are　becoming　useful　for　performing　forward/inverse　modeling　for　active/passive　components　to　enhance　a　circuit　design.　With　measured　or　simulated　data　of　microwave　devices,　ANNs　can　be　trained　to　learn　relevant　microwave　relationships,　which　are,　otherwise,　computationally　expensive　or　for　which　efficient　analytical　formulas　are　not　available.　Fundamental　concepts　of　the　ANN　structure　and　training,　such　as　feedforward　neural　networks　(FFNNs),　recurrent　neural　networks　(RNNs)/dynamic　neural　networks　(DNNs)/time-delay　neural　networks　(TDNNs),　deep　neural　networks,　and　neural　network　training　and　extrapolation,　are　described.　Knowledge-based　neural　networks　(KBNNs)　are　described　for　improving　the　accuracy　and　reliability　of　modeling　and　design　optimization.　Various　advanced　ANN　techniques,　such　as　neuro-transfer　function　(neuro-TF)　modeling,　neural　network　inverse　modeling,　and　deep　neural　network　modeling,　are　discussed.　The　existing　and　emerging　applications　of　ANN　in　microwave　CAD　are　identified,　such　as　electromagnetic　(EM)/multiphysics　modeling,　modeling　of　nonlinear　circuits　and　transistors,　filter　design,　very　large-scale　integration　(VLSI)　interconnects,　oscillator,　transmitter　and　receiver　modeling,　and　CAD　applications　in　such　as　gallium　nitride　(GaN)　high　electron-mobility　transistor　(HEMT),　wireless　power　transfer　(WPT),　microelectromechanical　system　(MEMS),　and　substrate-integrated　waveguide　(SIW).