Applied Materials, Inc.

Binbin Wang

Anshul Vyas

Liem Ferryanto

Ravi C Edupuganti

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.

Bayesian decomposition for mismatched performances in semiconductor equipment

A method of detecting failure causes in semiconductor processing systems may include receiving an indication of a failure in a semiconductor processing system and providing the indication of the failure as a query to a network representing the semiconductor processing system. The network may include nodes representing on-wafer effects and component functions, and relationships between the nodes that represent causal dependencies between the component functions and the on-wafer effects. The method may also include calculating a change in probabilities assigned to nodes representing the component functions resulting from the query, and generating an output indicating a probability of at least one of the component functions as a cause of the failure.

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San Jose, CA, United States of America

Binbin Wang