用CNN自动识别模拟电路版图中的可生成模块，提升转换效率。
A CNN-Based Technique to Assist Layout-to-Generator Conversion for Analog Circuits
- 基于CNN模型自动检测可由现有生成器创建的子模块
- 在4885个实例中实现99.3%分类精度，仅需18秒完成人工需88分钟的任务
- 能准确识别训练集外的陌生子模块，适合集成电路自动化设计人员
本文提出一种基于卷积神经网络（CNN）的技术，用于将模拟电路参考版图高效转换为过程式版图生成器。该模型自动识别库中可用生成器脚本可生成的子模块，并建议在生成器软件中以层级正确的方式使用。实验针对高速有线接收器版图，共包含4,885个子模块实例及145种不同设计。CNN将子模块分类为51类可生成和1类不可生成类别，其中不可生成类表示无可用生成器支持。模型对145种设计的分类精度达99.3%。相比人工检查耗时88分钟，该模型仅需18秒即可完成。此外，模型还能准确识别与训练数据差异较大的陌生子模块。
原文摘要 · Abstract (English)
We propose a technique to assist in converting a reference layout of an analog circuit into the procedural layout generator by efficiently reusing available generators for sub-cell creation. The proposed convolutional neural network (CNN) model automatically detects sub-cells that can be generated by available generator scripts in the library, and suggests using them in the hierarchically correct places of the generator software. In experiments, the CNN model examined sub-cells of a high-speed wireline receiver that has a total of 4,885 sub-cell instances including different 145 sub-cell designs. The CNN model classified the sub-cell instances into 51 generatable and one not-generatable classes. One not-generatable class indicates that no available generator can generate the classified sub-cell. The CNN model achieved 99.3% precision in examining the 145 different sub-cell designs. The CNN model greatly reduced the examination time to 18 seconds from 88 minutes required in manual examination. Also, the proposed CNN model could correctly classify unfamiliar sub-cells that are very different from the training dataset.
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