Wafer Level Spatial Signature Grouping Using Transfer Learning
AssigneeKLA Corporation
InventorsNarayani Narasimhan, Alán Dávila, P. Jithendra Kumar Reddy, Ski Sim, Osamu Yamamoto
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Abstract
A wafer map is classified using the machine learning based model and a signature on the wafer map. The machine learning based model uses transfer learning. The machine learning based model can be trained using images from various sources that are extracted and augmented and their features extracted. These extracted features can be classified into defects that occur during semiconductor manufacturing.
Claims20
A method comprising:
- receiving, a wafer map at a processor, wherein the wafer map is for a surface of an entire wafer, wherein the processor is configured to run a machine learning based model for classifying the wafer map;
- and
- classifying the wafer map using the machine learning based model and a signature on the wafer map, wherein the machine learning based model uses transfer learning.
A system comprising:
- a semiconductor wafer inspection system;
- and
- a processor in electronic communication with the semiconductor wafer inspection system, wherein the processor is configured to:
- receive a wafer map, wherein the wafer map is for a surface of an entire wafer, wherein the processor is configured to run a machine learning based model for classifying the wafer map;
- and
- classify the wafer map using the machine learning based model and a signature on the wafer map, wherein the machine learning based model uses transfer learning.
A non-transitory computer-readable storage medium, comprising one or more programs for executing the following steps on one or more computing devices:
- classify a wafer map using a machine learning based model and a signature on the wafer map, wherein the machine learning based model uses transfer learning, and wherein the wafer map is for a surface of an entire wafer.
Dependencies are inferred from the claim text.