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Analog and mixed-signal (AMS) computer-aided design tools are of increasing interest owing to demand for the wide range of AMS circuit specifications in the modern system on a chip and faster time to market requirement. Traditionally, to accelerate the design process, the AMS system is decomposed into smaller components (called modules ) such that the complexity and evaluation of each module are more manageable. However, this decomposition poses an interface problem, where the module’s input-output states deviate from when combined to construct the AMS system, and thus degrades the system expected performance. In this article, we develop a tool module-linking-graph assisted hybrid parameter search engine with neural networks (MOHSENN) to overcome these obstacles. We propose a module-linking-graph that enforces equality of the modules’ interfaces during the parameter search process and apply surrogate modeling of the AMS circuit via neural networks. Further, we propose a hybrid search consisting of a global optimization with fast neural network models and a local optimization with accurate SPICE models to expedite the parameter search process while maintaining the accuracy. To validate the effectiveness of the proposed approach, we apply MOHSENN to design a successive approximation register analog-to-digital converter in 65-nm CMOS technology. This demonstrated that the search time improves by a factor of 5 and 700 compared to conventional hierarchical and flat design approaches, respectively, with improved performance.