US 2020/0027021 A1Published application

Reinforcement learning for multi-domain problems

OwnerKumara Sastry, Kenny K. Toh +5inventors

InventorsKumara Sastry, Kenny K. Toh, John A. Swanson, Vivek K. Singh, Matthew K. Gumbel, Manuj Swaroop, Selim Dogru

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Abstract

Reinforcement learning methods are applied to the multi-domain problem of developing photoresist models for advanced semiconductor technologies. In an iterative process, candidate photoresist models are selected or generated, with each model comprising an optical imaging model, one or more analytical chemistry or deformation kernels, and one or more photoresist development model terms. Model parameters to be calibrated in an iteration are selected. The candidate photoresist models are calibrated to best fit photoresist contours extracted from SEM images. Values for the calibration model parameters are determined and the most useful analytical kernels are kept in each model while the others are dropped. A genetic algorithm uses the best calibrated photoresist models from the prior iteration to develop candidate models for the next iteration. The process iterates until no further accuracies can be gained. A residual minimization model can be trained to correct for residual errors in the final model.

Claims25

Dependent claims

A method comprising:

(i) determining a plurality of candidate photoresist models, individual of the candidate photoresist models comprising one or more analytical kernels;
(ii) for individual of the candidate photoresist models, developing a calibrated candidate photoresist model by fitting the individual candidate photoresist model to training photoresist contours extracted from scanning electron microscopy (SEM) image data;
(iii) selecting the calibrated candidate photoresist model from among the calibrated candidate photoresist models that best predicts the training photoresist contours as a best calibrated candidate photoresist model;
(iv) iteratively repeating (i) through (iii) until the predictive accuracy of the best calibrated candidate photoresist model of a current iteration of (i) through (iii) is no better than the predictive accuracy of the best calibrated candidate photoresist model of a prior iteration of (i) through (iii), the plurality of candidate photoresist models determined in a next iteration of (i) through (iii) being determined at least in part by a genetic algorithm operating on at least the best calibrated candidate photoresist model from the current iteration;
(v) identifying the best calibrated candidate photoresist model from a last iteration of (i) through (iii) as a final photoresist model;
and
(vi) using the final photoresist model to predict device photoresist contours based at least in part on device photomask data.

4 more elements

A system comprising:

one or more processors;
and
one or more computer-readable storage media storing instructions thereon for causing the one or more processors to perform a method comprising:
(i) determining a plurality of candidate photoresist models, individual of the candidate photoresist models comprising one or more analytical kernels;
(ii) for individual of the candidate photoresist models, developing a calibrated candidate photoresist model by fitting the individual candidate photoresist model to training photoresist contours extracted from scanning electron microscopy (SEM) image data;
(iii) selecting the calibrated candidate photoresist model from among the calibrated candidate photoresist models that best predicts the training photoresist contours as a best calibrated candidate photoresist model;
(iv) iteratively repeating (i) through (iii) until the predictive accuracy of the best calibrated candidate photoresist model of a current iteration of (i) through (iii) is no better than the predictive accuracy of the best calibrated candidate photoresist model of a prior iteration of (i) through (iii), the plurality of candidate photoresist models determined in a next iteration of (i) through (iii) being determined at least in part by a genetic algorithm operating on at least the best calibrated candidate photoresist model from the current iteration;
(v) identifying the best calibrated candidate photoresist model from a last iteration of (i) through (iii) as a final photoresist model;
and
(vi) using the final photoresist model to predict device photoresist contours based at least in part on device photomask data.

7 more elements

One or more computer-readable storage media storing instructions thereon for causing a computing device to perform a method comprising:

(i) determining a plurality of candidate photoresist models, individual of the candidate photoresist models comprising one or more analytical kernels;
(ii) for individual of the candidate photoresist models, developing a calibrated candidate photoresist model by fitting the individual candidate photoresist model to training photoresist contours extracted from scanning electron microscopy (SEM) image data;
(iii) selecting the calibrated candidate photoresist model from among the calibrated candidate photoresist models that best predicts the training photoresist contours as a best calibrated candidate photoresist model;
(iv) iteratively repeating (i) through (iii) until the predictive accuracy of the best calibrated candidate photoresist model of a current iteration of (i) through (iii) is no better than the predictive accuracy of the best calibrated candidate photoresist model of a prior iteration of (i) through (iii), the plurality of candidate photoresist models determined in a next iteration of (i) through (iii) being determined at least in part by a genetic algorithm operating on at least the best calibrated candidate photoresist model from the current iteration;
(v) identifying the best calibrated candidate photoresist model from a last iteration of (i) through (iii) as a final photoresist model;
and
(vi) using the final photoresist model to predict device photoresist contours based at least in part on device photomask data.

4 more elements

Dependencies are inferred from the claim text.