发表机构
KU Leuven; imec（鲁汶大学; 比利时微电子研究中心）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究提出一种从半导体设备日志时间序列推断工艺动力学的信息论因果建模框架，经实验验证其可揭示新型因果关系，但存在部分与工艺知识不一致的结果，需进一步改进。
AI 中文摘要
随着半导体行业向更复杂的计算器件和更严格的工艺容差发展，先进工艺控制变得至关重要。本研究探索一种新颖框架，直接从原始设备日志文件时间序列数据推断半导体工艺的潜在动力学。通过将设备动力学建模为包含确定性分量和随机分量的随机动力学系统，我们通过Liang-Kleeman和Pires形式主义估计变量间的熵转移率。初步结果显示，推断出的依赖关系中7.5%为已知，36.0%合理，17.5%代表此前未表征的关系，39.0%与已建立的工艺知识不一致。这些发现证明该框架具备揭示新型因果见解的能力，同时推动进一步改进以减少不一致结果。
英文摘要
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.
CommentsTo be presented at the 2026 IEEE 33rd International Conference on Electronics, Circuits and Systems (ICECS), and published by IEEE in the conference proceedings
