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For the first time, an adjoint neural network method is introduced for sensitivity analysis in neural-based microwave modeling and design. Exact first and second order sensitivities are systematically calculated for generic microwave neural models including variety of knowledge based neural models embedding microwave empirical information. A new formulation allows the models to learn both the input/output behavior of the modeling problem and its derivative data simultaneously. Examples for passive and active microwave modeling and simulation are presented.