In advanced nodes, optimization of power, performance and area (PPA) has become highly complex and challenging. Machine learning (ML) and design-technology co-optimization (DTCO) provide promising mitigations, but face limitations due to a lack of diverse training data as well as long design flow turnaround times (TAT). We propose ArtNet, a novel artificial netlist generator designed to tackle these issues. Unlike previous methods, ArtNet replicates key topological characteristics, enhancing ML model generalization and supporting broader design space exploration for DTCO. By producing realistic artificial datasets that moreclosely match given target parameters, ArtNet enables more efficient PPAoptimization and exploration of flows and design enablements. In the context of CNN-based DRV prediction, ArtNet's data augmentationimproves F1 score by 0.16 compared to using only the original (real) dataset. In the DTCO context, ArtNet-generated mini-brains achieve a PPA match up to 97.94%, demonstrating close alignment with design metrics of targeted full-scale block designs.

翻译：在先进工艺节点中，功耗、性能与面积（PPA）的优化变得高度复杂且充满挑战。机器学习（ML）与设计-工艺协同优化（DTCO）虽提供了有前景的解决方案，但受限于缺乏多样化的训练数据以及设计流程周转时间（TAT）过长的问题。本文提出ArtNet，一种新颖的人工网表生成器，旨在解决这些难题。与现有方法不同，ArtNet能够复现关键拓扑特征，从而增强ML模型的泛化能力，并支持更广泛的DTCO设计空间探索。通过生成更贴近给定目标参数的真实人工数据集，ArtNet实现了更高效的PPA优化，并促进了流程与设计可行方案的探索。在基于CNN的DRV预测任务中，ArtNet的数据增强使F1分数相较于仅使用原始（真实）数据集提升了0.16。在DTCO应用中，ArtNet生成的小规模脑图实现了高达97.94%的PPA匹配度，显示出与目标全尺寸模块设计指标的高度吻合。