In this article, a new Neuro-Space mapping method is presented aimed at using neural networks to automatically enhance nonlinear device models, such as FET models. Compared with previously published space mapping methods, our proposed method produces better modeling accuracy and provides more effective combinations of mapping structure with existing coarse model. In our proposed models, separate mappings for voltage and current at gate and drain are used as the mapping structure. Training methods for mapping neural networks are also proposed. Application examples on modeling MESFET devices and the use of new models in DC, S-parameter and combined DC and S-parameter simulation demonstrate that our proposed Neuro-Space mapping model matches more closely with the device data than that by the traditional Neuro-Space mapping method for modeling nonlinear microwave devices.