Abstract: With the develepment of microelectronics technology, the cut-off frequency of the silicon-based CMOS devices has reached the millimeter-wave band. It makes it possible to realize silicon-based microwave monolithic integrated circiuts. Therefore, it becomes necessary to establish the model of silicon-based millimeter-wave coplanar waveguide for accurate design of silicon microwave monolithic integrated circuits. Silicon-based millimeter-wave coplanar waveguide (CPW) scalable model based on neural network technique is proposed in this paper. A three-layers neural network structure is used. Neural network is adopted to learn the mapping between the geometrical variables and S parameter of the coplanar waveguide from measured results of CPW. Comparison of simulation and measurement results shows that CPW scalable models based on neural network can provide accurate and fast prediction of the S parameters of CPW for differential physical sizes as variables.