A Module-Linking Graph Assisted Hybrid Optimization Framework for Custom Analog and Mixed-Signal Circuit Parameter Synthesis

Abstract

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.