Information-Theoretic Causal Modelling of Semiconductor Process Dynamics

[2608.14678] Information-Theoretic Causal Modelling of Semiconductor Process Dynamics Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Electrical Engineering and Systems Science > Signal Processing arXiv:2608.14678 (eess) [Submitted on 4 Aug 2026] Title: Information-Theoretic Causal Modelling of Semiconductor Process Dynamics Authors: Daniel Sørensen , Giorgio Melchiorre , Sudip Bandyopadhyay , Sandip Halder , Roel Wuyts , Bappaditya Dey View a PDF of the paper titled Information-Theoretic Causal Modelling of Semiconductor Process Dynamics, by Daniel S{\o}rensen and 5 other authors View PDF HTML (experimental) Abstract: 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. Comments: To be presented at the 2026 IEEE 33rd International Conference on Electronics, Circuits and Systems (ICECS), and published by IEEE in the conference proceedings Subjects: Signal Processing (eess.SP) ; Artificial Intelligence (cs.AI); Information Theory (cs.IT) Cite as: arXiv:2608.14678 [eess.SP] (or arXiv:2608.14678v1 [eess.SP] for this version) https://doi.org/10.48550/arXiv.2608.14678 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Daniel Sorensen [ view email ] [v1] Tue, 4 Aug 2026 12:27:19 UTC (533 KB) Full-text links: Access Paper: View a PDF of the paper titled Information-Theoretic Causal Modelling of Semiconductor Process Dynamics, by Daniel S{\o}rensen and 5 other authors View PDF HTML (experimental) TeX Source view license Current browse context: eess.SP < prev | next > new | recent | 2026-08 Change to browse by: cs cs.AI cs.IT eess math math.IT References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab) Major funding support from

Related documents

• Chance-changing causal processes#700293
• Recursion as a Phase Transition in Causal Dependence#708996
• Modeling and Control of a Chemical Process Network Using Physics-Informed Transfer Learning#288530
• On the relaxation problem in statistical mechanics#820701
• Beyond Conditional Independence: Root Cause Analysis with Deep Causal Models#1131972