Abstract Enhancing the competitive advantages of wafer fabs is crucial to increase the number of gross dies per wafer and to reduce average die cost. Most of existing studies about IC (integrated circuit) feature design focus on yield enhancement, yet little research has been done on cost reduction through increasing gross die number and decreasing shot number simultaneously. To generate the alternative feature design to improve wafer exposure effectiveness, a prediction model between chip size and gross die number and shot number was built through amount of data collection and model training. However, it’s difficult to consider different exposure conditions under various parameter setting of amount of IC features. In order to fill the gap of considering real setting, this study aims to propose a two-phase non-dominated sorting particle swarm optimization (TNSPSO) method to maximize number of gross die and minimize the shot number and then suggests alternative chip features for IC designers. First, non-dominated sorting algorithm is used to find the solutions on the frontier. Second, these particles on the frontier are diffused toward the sparse region on the frontier. To evaluate the validity of proposed approach, two conventional heuristic algorithms, non-dominated sorting genetic algorithm II (NSGA-II) and multi-objective particle swarm optimization (MOPSO) were selected . The experiment results showed that the proposed method not only capture the solutions closer to the Pareto frontier but also has better convergence and diversity of the solutions than the other methods. The proposed approach can assist IC designer in effectively deriving chip layout design with enhancement of wafer exposure effectiveness.

中文翻译：

---

  用于芯片特征设计的两阶段非支配排序粒子群优化以提高晶圆曝光效率

摘要 提高晶圆厂的竞争优势对于增加每个晶圆的总裸片数量和降低平均裸片成本至关重要。大多数关于 IC（集成电路）特征设计的现有研究都集中在提高产量上，但很少有研究通过同时增加总管芯数量和减少镜头数量来降低成本。为了生成替代特征设计以提高晶圆曝光效率，通过大量数据收集和模型训练建立了芯片尺寸与总裸片数和镜头数之间的预测模型。然而，在不同的IC特征量参数设置下，很难考虑不同的曝光条件。为了填补考虑实际设置的空白，本研究旨在提出一种两阶段非支配排序粒子群优化 (TNSPSO) 方法，以最大限度地增加总裸片数量并最大限度地减少镜头数量，然后为 IC 设计人员建议替代芯片功能。首先，使用非支配排序算法在边界上寻找解。其次，边界上的这些粒子向边界上的稀疏区域扩散。为了评估所提出方法的有效性，选择了两种常规启发式算法，即非支配排序遗传算法 II (NSGA-II) 和多目标粒子群优化 (MOPSO)。实验结果表明，所提出的方法不仅能捕捉到更接近帕累托边界的解，而且​​比其他方法具有更好的收敛性和多样性。