机器学习辅助新产品设置

J. A. Torres, Ivan Kissiov, M. Essam, C. Hartig, Richard Gardner, Ken Jantzen, Stefan Schueler, M. Niehoff
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引用次数: 0

摘要

过去,关键区域分析等概念已经成功地应用于预测随机和系统布局引起的影响。这使半导体公司能够初步估计固定的工艺如何响应各种不同的设计。然而,随着单个产品数量的增加,以及每个产品晶圆总数的减少,确定哪种工艺参数将导致每个单独产品的最高可能良率变得越来越困难。我们概述了一种使用机器学习的方法,该方法结合了过程和设计数据,大大减少了设置新产品所需的时间。我们已经证明,类似的设计(基于我们的特征提取)在晶圆厂中的行为相似,从而允许我们构建模型,最终可用于找到给定设计的最佳工艺条件。由于性质或过程优化,该方法还探索了SHAPley加性解释(SHAP)的使用,以便与现有的观测结果的人类和物理解释“接口”,从而提供了一种评估数值推导模型的质量和可靠性的机制。
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Machine Learning Assisted New Product Setup
In the past, concepts like critical area analysis, have been successfully implemented to predict random and systematic layout induced effects. This has enabled semiconductor companies to have an initial estimate as to how a fixed process will respond to a variety of different designs. However, as the number of individual products increases, along with a reduction in the total number of wafers per product, it becomes increasingly difficult to determine which process parameters will lead to the highest possible yield for each individual product. We have outlined a methodology using machine learning that combines process and design data to greatly reduce the time needed for setting up new products. We have shown that similar designs (based on our feature extraction) behave similarly in the fab, thus allowing us to construct models that can eventually be used to find the optimal process conditions for a given design. Due to the nature or process optimization, this methodology also explores the use of SHAPley additive explanations (SHAP) in order to “interface” with existing human and physical explanations of the observations, thus providing a mechanism to assess the quality and reliability of the numerically derived models.
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