Machine learning model for asymmetry-induced overlay error correction
AssigneeASML Netherlands B.V.
InventorsKui-Jun HUANG, Liping REN
Life of this patent
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Abstract
A correction to an error of overlay measurement which accounts for target structure asymmetry using a neural network is described. According to embodiments, an overlay measurement accuracy can be improved by accounting for multiple and/or asymmetric perturbations in the target structure. A trained neural network is described which generates a correction value for overlay measurement based on a measure of asymmetry. Based on an as-measured overlay measurement, which may not account for target structure asymmetry, and the correction value, a true overlay measurement is determined-which can exhibit improved accuracy and reduced uncertainty versus uncorrected values.
Claims21
-16. (canceled)
Non-transitory, machine-readable media having instructions therein, the instructions, when executed by one or more processors, configured to cause the one or more processors to at least:
- obtain a measure of asymmetry, wherein the measure of asymmetry is based, at least in part, on an electromagnetic measurement of a target structure;
- and
- determine, by a hardware computer and based at least in part on a trained machine learning model, a measure of overlay for the target structure based on the measure of asymmetry.
A method comprising:
- obtaining a measure of asymmetry, wherein the measure of asymmetry is based, at least in part, on an electromagnetic measurement of a target structure;
- and
- determine, by a hardware computer system and based at least in part on a trained machine learning model, a measure of overlay for the target structure based on the measure of asymmetry.
Dependencies are inferred from the claim text.