Source and mask optimization (SMO) is a widely used computational lithography technology that greatly improves the image fidelity of lithography systems. This paper develops an efficient informatics-based SMO (EISMO) method to improve the image fidelity of lithography systems. First, a communication channel model is established to depict the mechanism of information transmission in the SMO framework, where the source is obtained from the gradient-based SMO algorithm. The manufacturing-aware mask distribution is then optimized to achieve the best mutual information, and the theoretical lower bound of lithography patterning error is obtained. Subsequently, an efficient informatics-based method is proposed to refine the mask optimization result in SMO, further reducing the lithography patterning error. It is shown that the proposed EISMO method is computationally efficient and can achieve superior imaging performance over the conventional SMO method.