We propose a UNet-based foundation model and its self-supervised learning method to address two key challenges: 1) lack of qualified annotated analog layout data, and 2) excessive variety in analog layout design tasks. For self-supervised learning, we propose random patch sampling and random masking techniques automatically to obtain enough training data from a small unannotated layout dataset. The obtained data are greatly augmented, less biased, equally sized, and contain enough information for excessive varieties of qualified layout patterns. By pre-training with the obtained data, the proposed foundation model can learn implicit general knowledge on layout patterns so that it can be fine-tuned for various downstream layout tasks with small task-specific datasets. Fine-tuning provides an efficient and consolidated methodology for diverse downstream tasks, reducing the enormous human effort to develop a model per task separately. In experiments, the foundation model was pre-trained using 324,000 samples obtained from 6 silicon-proved manually designed analog circuits, then it was fine-tuned for the five example downstream tasks: generating contacts, vias, dummy fingers, N-wells, and metal routings. The fine-tuned models successfully performed these tasks for more than one thousand unseen layout inputs, generating DRC/LVS-clean layouts for 96.6% of samples. Compared with training the model from scratch for the metal routing task, fine-tuning required only 1/8 of the data to achieve the same dice score of 0.95. With the same data, fine-tuning achieved a 90% lower validation loss and a 40% higher benchmark score than training from scratch.

翻译：本文提出一种基于UNet的基础模型及其自监督学习方法，旨在解决两大关键挑战：1）合格标注模拟版图数据的匮乏；2）模拟版图设计任务的过度多样性。针对自监督学习，我们提出随机图块采样与随机掩码技术，能够从小型无标注版图数据集中自动获取充足的训练数据。所获数据经过大幅增强，具有偏差小、尺寸统一的特点，且包含足够信息以覆盖各类合格版图模式的过度多样性。通过使用所获数据进行预训练，所提出的基础模型能够学习版图模式的隐式通用知识，从而可利用小型任务特定数据集针对多种下游版图任务进行微调。微调为多样化下游任务提供了高效统一的方法论，显著减少了为每个任务单独开发模型所需的大量人力。实验中，基础模型使用从6个经过硅验证的手工设计模拟电路中获得的324,000个样本进行预训练，随后针对五个示例下游任务进行微调：生成接触孔、通孔、虚拟指状结构、N阱和金属布线。微调模型成功对上千个未见版图输入执行了这些任务，为96.6%的样本生成了通过DRC/LVS验证的洁净版图。相较于从零开始训练金属布线任务模型，微调仅需1/8的数据量即可达到相同的0.95 Dice分数。在相同数据量下，微调比从零训练降低了90%的验证损失，并提升了40%的基准分数。