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.

翻译：我们提出了一种技术，旨在通过高效复用现有子单元生成器，辅助将模拟电路的参考版图转换为程序化版图生成器。所提出的卷积神经网络（CNN）模型能够自动检测库中可用生成器脚本可生成的子单元，并在生成器软件的层次结构正确位置推荐使用它们。在实验中，CNN模型检测了一个高速有线接收器的子单元，该接收器共包含4,885个子单元实例，涵盖145种不同的子单元设计。CNN模型将这些子单元实例分类为51个可生成类别和一个不可生成类别。不可生成类别表示没有可用生成器能够生成该分类的子单元。在检测145种不同子单元设计时，CNN模型的精确度达到99.3%。该模型将检测时间从手动检测所需的88分钟大幅缩短至18秒。此外，所提出的CNN模型能够正确分类与训练数据集差异显著的不熟悉子单元。