提出新方法解决芯片制造中设备匹配难题，无需参考标准且适配异构设备。
Tool-to-Tool Matching Analysis Based Difference Score Computation Methods for Semiconductor Manufacturing
- 基于数据方差与模态数变化检测设备不匹配
- 单变量方法相关系数超0.95，多变量方法超0.75
- 适用于不同厂商异构设备，对参数敏感性可分析
针对半导体制造设备中的工具到工具匹配（TTTM）问题，传统方法依赖静态配置或难以获取的基准参考，且在异构设备场景下表现不佳。本文提出新型分析流程，假设不匹配设备的数据具有更高方差和更多模态。最佳单变量方法在方差与模态数上的相关系数分别超过0.95和0.5，证明方法有效；最优多变量方法与顶尖单变量方法的相关系数超过0.75，显示其性能。最后，分析了多变量算法对超参数的敏感性。
原文摘要 · Abstract (English)
We consider the problem of tool-to-tool matching (TTTM), also called, chamber matching in the context of a semiconductor manufacturing equipment. Traditional TTTM approaches utilize static configuration data or depend on a golden reference which are difficult to obtain in a commercial manufacturing line. Further, existing methods do not extend very well to a heterogeneous setting, where equipment are of different make-and-model, sourced from different equipment vendors. We propose novel TTTM analysis pipelines to overcome these issues. We hypothesize that a mismatched equipment would have higher variance and/or higher number of modes in the data. Our best univariate method achieves a correlation coefficient >0.95 and >0.5 with the variance and number of modes, respectively showing that the proposed methods are effective. Also, the best multivariate method achieves a correlation coefficient >0.75 with the top-performing univariate methods, showing its effectiveness. Finally, we analyze the sensitivity of the multivariate algorithms to the algorithm hyper-parameters.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。