Minimally supervised learning for determining causes of outlying data points
AssigneeDelta Design, Inc.
InventorCharles Lincoln Parker
Life of this patent
Estimating the term…
Abstract
A system may identify anomalous output among a plurality of outputs at a process step in the manufacturing process. A system may receive manufacturing attributes associated with each of the plurality of outputs including the anomalous output. A system may build, using a machine learning model, at least one isolation tree model comprising a plurality of parent nodes each corresponding to a split condition of one of the manufacturing attributes and a leaf node corresponding to the anomalous output, wherein each of the parent nodes of the at least one isolation tree model is associated with a manufacturing attribute directly leading to the anomalous output.
Claims25
A method of associating an anomalous output of a semiconductor manufacturing process with a manufacturing attribute of the manufacturing process, the method comprising:
- identifying the anomalous output among a plurality of outputs at a process step in the manufacturing process;
- receiving manufacturing attributes associated with each of the plurality of outputs including the anomalous output;
- and
- building, using a machine learning model, at least one isolation tree model comprising a plurality of parent nodes each corresponding to a split condition of one of the manufacturing attributes and a leaf node corresponding to the anomalous output,
- wherein each of the parent nodes of the at least one isolation tree model is associated with a manufacturing attribute directly leading to the anomalous output.
Non-transitory computer readable storage media storing instructions that when executed by a system of one or more processors, cause the one or more processors to:
- identify anomalous output among a plurality of outputs at a process step in a semiconductor manufacturing process;
- receive manufacturing attributes associated with each of the plurality of outputs including the anomalous output;
- and
- build, using a machine learning model, at least one isolation tree model comprising a plurality of parent nodes each corresponding to a split condition of one of the manufacturing attributes and a leaf node corresponding to the anomalous output,
- wherein each of the parent nodes of the at least one isolation tree model is associated with a manufacturing attribute directly leading to the anomalous output.
-36. (canceled)
Dependencies are inferred from the claim text.