用机器学习预测模拟顶层中数字模块的可实现性，加速芯片设计迭代。
Machine Learning-based feasibility estimation of digital blocks in BCD technology
- 基于高阶特征的机器学习模型评估数字模块可行性
- 避免繁琐布图布线试验，提升设计效率
- 适合模拟与数字后端协同设计人员使用
模拟顶层混合信号集成电路设计耗时且主要依赖人工。在该流程中，顶层集成者通常预留区域用于放置数字模块。该区域的尺寸和形状等特征对实现所需功能的数字逻辑具有重要影响。本文提出一种基于机器学习的评估方法，利用一组高阶特征预测数字实现的可行性。该方法旨在避免耗时的布图布线试验，实现在顶层布局阶段为数字与模拟后端设计者提供快速反馈。
原文摘要 · Abstract (English)
Analog-on-Top Mixed Signal (AMS) Integrated Circuit (IC) design is a time-consuming process predominantly carried out by hand. Within this flow, usually, some area is reserved by the top-level integrator for the placement of digital blocks. Specific features of the area, such as size and shape, have a relevant impact on the possibility of implementing the digital logic with the required functionality. We present a Machine Learning (ML)-based evaluation methodology for predicting the feasibility of digital implementation using a set of high-level features. This approach aims to avoid time-consuming Place-and-Route trials, enabling rapid feedback between Digital and Analog Back-End designers during top-level placement.
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