Similar to 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 the data are major challenges to efficiently, yet accurately, diagnose rare or new system issues by merely using model-based approaches. To reliably narrow down the causal search space, we validate a ranking algorithm that applies transfer entropy for bivariate interaction analysis of a system's multivariate time series to obtain a weighted directed graph, and graph eigenvector centrality to identify the system's most important sources of original information or causal influence. The results suggest that this approach robustly identifies the true drivers or causes of a complex system's deviant behavior, even when its reconstructed information transfer network includes redundant edges.
Keywords: complex systems; coupled Lorenz systems; eigenvector centrality; node importance; original information; time series; transfer entropy.