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.

中文翻译：

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用于自定义模拟和混合信号电路参数合成的模块链接图辅助混合优化框架

由于现代片上系统对广泛的 AMS 电路规范的需求以及更快的上市时间要求，模拟和混合信号 (AMS) 计算机辅助设计工具越来越受到关注。传统上，为了加速设计过程，AMS 系统被分解成更小的组件（称为模块) 使得每个模块的复杂性和评估更易于管理。然而，这种分解带来了一个接口问题，模块的输入输出状态在组合构建 AMS 系统时会偏离，从而降低系统的预期性能。在本文中，我们开发了一个工具模块链接图辅助混合参数搜索引擎与神经网络（MOHSENN）来克服这些障碍。我们提出了一个模块链接图，它在参数搜索过程中强制模块接口的相等性，并通过神经网络应用 AMS 电路的代理建模。此外，我们提出了一种混合搜索，包括具有快速神经网络模型的全局优化和具有精确 SPICE 模型的局部优化，以在保持准确性的同时加快参数搜索过程。为了验证所提出方法的有效性，我们应用 MOHSENN 设计了一个采用 65-nm CMOS 技术的逐次逼近寄存器模数转换器。这表明，与传统的分层和平面设计方法相比，搜索时间分别提高了 5 倍和 700 倍，性能也有所提高。