Bayesian decomposition for mismatched performances in semiconductor equipment

Abstract

Bayesian inference and modeling techniques, along with model decomposition may be used to improve mismatch performances in semiconductor processing devices by identifying sources of intrinsic and extrinsic variations in performance. A network of causal relationships between processes and hardware in a semiconductor processing device may be accessed to generate a first Bayesian model for a first semiconductor processing device using the causal relationships in the network and performance data. A second Bayesian model may also be generated for a second semiconductor processing device using the causal relationships in the network and associated performance data. Response distributions generated by the first Bayesian model and the second Bayesian model may be compared to determine whether a performance of the of the first semiconductor processing device matches a performance of the second semiconductor processing device by decomposing the model transfer functions to identify the effects of intrinsic/extrinsic variables.