Detecting outliers and anomalies for ocd metrology machine learning
OwnerEitan A. Rothstein, Yongha Kim +4inventors
InventorsEITAN A. ROTHSTEIN, YONGHA KIM, ILYA RUBINOVICH, ARIEL BROITMAN, OLGA KRASNYKOV, BARAK BRINGOLTZ
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Abstract
A system and methods for OCD metrology are provided including receiving training data for training an OCD machine learning (ML) model, including multiple pairs of corresponding sets of scatterometric data and reference parameters. For each of the pairs, one or more corresponding outlier metrics are by calculated and corresponding outlier thresholds are applied whether a given pair is an outlier pair. The OCD ML model is then trained with the training data less the outlier pairs.
Claims20
A method for OCD metrology, comprising:
- receiving training data for training an OCD machine learning (ML) model, wherein the training data includes multiple pairs of corresponding input and output training data, wherein the input data of each pair includes a set of scatterometric data measured from a wafer pattern and wherein the output data of each pair includes a reference parameter measured from the same wafer pattern;
- for each of the pairs, calculating one or more corresponding outlier metrics, by calculating one or more of 1) a difference between the reference parameter and a corresponding predicted parameter that is predicted from the corresponding set of scatterometric data, 2) a difference between a merit function of the set of scatterometric data and a deviation from a distribution of merit functions, and 3) a difference between the reference parameter and a mean or a median of reference parameters;
- for each of the one or more outlier metrics of each of the pairs, applying a corresponding outlier threshold to the outlier metric to determine whether the pair is an outlier pair, to determine one or more outlier pairs in the training data;
- and
- training the OCD ML model with the training data less the one or more outlier pairs, to train the OCD ML model to predict, from new scatterometric data, respective new predicted parameters.
A system for OCD metrology compromising a processor having non-transient memory, the memory including instructions that when executed by the processor cause the processor to implement steps of:
- receiving training data for training an OCD machine learning (ML) model, wherein the training data includes multiple pairs of corresponding input and output training data, wherein the input data of each pair includes a set of scatterometric data measured from a wafer pattern and wherein the output data of each pair includes a reference parameter measured from the same wafer pattern;
- for each of the pairs, calculating one or more corresponding outlier metrics, by calculating one or more of 1) a difference between the reference parameter and a corresponding predicted parameter that is predicted from the corresponding set of scatterometric data, 2) a difference between a merit function of the set of scatterometric data and a deviation from a distribution of merit functions, and 3) a difference between the reference parameter and a mean or a median of reference parameters;
- for each of the one or more outlier metrics of each of the pairs, applying a corresponding outlier threshold to the outlier metric to determine whether the pair is an outlier pair, to determine one or more outlier pairs in the training data;
- and
- training the OCD ML model with the training data less the one or more outlier pairs, to train the OCD ML model to predict, from new scatterometric data, respective new predicted parameters.
A non-transitory, machine-accessible storage medium having instructions stored thereon, the instructions, when executed by a machine, causing the machine to implement steps of:
- receiving training data for training an OCD machine learning (ML) model, wherein the training data includes multiple pairs of corresponding input and output training data, wherein the input data of each pair includes a set of scatterometric data measured from a wafer pattern and wherein the output data of each pair includes a reference parameter measured from the same wafer pattern;
- for each of the pairs, calculating one or more corresponding outlier metrics, by calculating one or more of 1) a difference between the reference parameter and a corresponding predicted parameter that is predicted from the corresponding set of scatterometric data, 2) a difference between a merit function of the set of scatterometric data and a deviation from a distribution of merit functions, and 3) a difference between the reference parameter and a mean or a median of reference parameters;
- for each of the one or more outlier metrics of each of the pairs, applying a corresponding outlier threshold to the outlier metric to determine whether the pair is an outlier pair, to determine one or more outlier pairs in the training data;
- and
- training the OCD ML model with the training data less the one or more outlier pairs, to train the OCD ML model to predict, from new scatterometric data, respective new predicted parameters.
Dependencies are inferred from the claim text.