  Like natural complex systems such as the Earth's climate or a living cell,
semiconductor lithography systems are characterized by nonlinear dynamics
across more than a dozen orders of magnitude in space and time. Thousands of
sensors measure relevant process variables at appropriate sampling rates, to
provide time series as primary sources for system diagnostics. However,
high-dimensionality, non-linearity and non-stationarity of data remain a major
challenge to effectively diagnose rare or new system issues by merely using
model-based approaches. To reduce the causal search space, we validate an
algorithm that applies transfer entropy to obtain a weighted directed graph
from a system's multivariate time series and graph eigenvector centrality to
identify the system's most influential parameters. The results suggest that
this approach robustly identifies the true influential sources in a complex
system, even when its information transfer network includes redundant edges.
