用信息论方法从设备日志中挖掘半导体工艺的因果动态关系。
Information-Theoretic Causal Modelling of Semiconductor Process Dynamics
- 基于李昂-克莱曼与皮雷斯理论，将设备数据建模为确定性与随机性组合系统。
- 发现36%关系合理、17.5%为新发现、39%与已有知识冲突。
- 适合芯片制造与先进工艺控制领域的研究人员参考。
随着半导体行业向更复杂的计算器件和更严苛的工艺公差发展，先进过程控制变得至关重要。本文提出一种新框架，直接从原始设备日志的时间序列数据中推断半导体工艺的内在动态。通过将工具动态建模为包含（a）确定性分量和（b）随机分量的随机动力系统，我们利用李昂-克莱曼与皮雷斯形式化方法估计变量间的熵转移速率。初步结果表明，所推断依赖关系中7.5%为已知，36.0%合理，17.5%代表此前未被识别的关系，39.0%与现有工艺知识不一致。这些发现展示了该框架揭示新因果洞见的能力，同时提示需进一步改进以减少矛盾结果。
原文摘要 · Abstract (English)
With the progress of the semiconductor industry toward increasingly complex compute devices and tighter process tolerances, advanced process control has become crucial. This work explores a novel framework to infer the underlying dynamics of semiconductor processes, directly from raw equipment log-file time-series data. By modelling the tool dynamics as a stochastic dynamical system comprising (a) a deterministic component and (b) a stochastic component, we estimate entropy transfer rates between variables through the Liang-Kleeman and Pires formalism. Preliminary results indicated that 7.5% of the inferred dependencies were known, 36.0% were plausible, 17.5% represented previously uncharacterised relationships, and 39.0% were inconsistent with established process knowledge. These findings demonstrate the framework's capability to uncover novel causal insights, while motivating further improvements to reduce inconsistent findings.
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。