Daniel Sørensen, Giorgio Melchiorre
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.
@article{5f7f4f98-75cc-4fb9-b87b-741e2af46299,
title={2026 Sorensen Semiconductor Process Dynamics},
author={Daniel Sørensen and Giorgio Melchiorre},
year={2026},
language={en}
}TY - JOUR TI - 2026 Sorensen Semiconductor Process Dynamics AU - Daniel Sørensen AU - Giorgio Melchiorre PY - 2026 LA - en ER -
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