Information-Theoretic Causal Modelling of Semiconductor Process Dynamics
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 unchar...
| Authors | Daniel Sørensen, Giorgio Melchiorre, Sudip Bandyopadhyay et al. |
|---|---|
| Primary category | eess.SP |
| Published | 4 August 2026 |
| Last updated at source | 4 August 2026 |
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