US 11,816,579 B2
Method and apparatus for detecting defect pattern on wafer based on unsupervised learning
Min Sik Chu, Seoul (KR); Seong Mi Park, Seoul (KR); Jiin Jeong, Seoul (KR); Jae Hoon Kim, Seoul (KR); Kyong Hee Joo, Seoul (KR); Ho Geun Park, Seoul (KR); and Baek Young Lee, Seoul (KR)
Assigned to SAMSUNG SDS CO., LTD., Seoul (KR)
Filed by SAMSUNG SDS CO., LTD., Seoul (KR)
Filed on Jan. 17, 2023, as Appl. No. 18/097,608.
Application 18/097,608 is a division of application No. 16/884,587, filed on May 27, 2020, granted, now 11,587,222.
Claims priority of application No. 10-2019-0063195 (KR), filed on May 29, 2019.
Prior Publication US 2023/0177347 A1, Jun. 8, 2023
Int. Cl. G06K 9/00 (2022.01); G06N 3/088 (2023.01); G06T 7/00 (2017.01); G06F 18/23 (2023.01); G06N 3/045 (2023.01); G06N 3/047 (2023.01); G06V 10/42 (2022.01); G06V 10/762 (2022.01); G06V 10/77 (2022.01); G06V 10/30 (2022.01); G06V 10/48 (2022.01)
CPC G06N 3/088 (2013.01) [G06F 18/23 (2023.01); G06N 3/045 (2023.01); G06N 3/047 (2023.01); G06T 7/001 (2013.01); G06V 10/30 (2022.01); G06V 10/431 (2022.01); G06V 10/48 (2022.01); G06V 10/7625 (2022.01); G06V 10/7715 (2022.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/30148 (2013.01)] 7 Claims
OG exemplary drawing
 
1. A method for detecting a defect pattern on a wafer, the method being performed by a computing device, and comprising:
obtaining binarized inspection data for each wafer including data indicating defectiveness of each of a plurality of chips, the binarized inspection data comprising a first axis coordinate based on a first axis, and a second axis coordinate based on a second axis;
mapping the binarized inspection data to a three-dimensional space based on the first axis, the second axis, and a third axis, wherein third axis coordinates correspond to the data indicating defectiveness;
generating a surface regression model through three-dimensional surface regression constructed by the binarized inspection data mapped to the three-dimensional space;
performing first noise removal based on three-dimensional spatial auto-correlation on the binarized inspection data mapped to the three-dimensional space to generate denoised binarized inspection data, wherein the performing the first noise removal comprises using the surface regression model,
extracting a feature from the denoised binarized inspection data, wherein the feature comprises a feature calculated based on a defective chip distribution pattern obtained as a result of density estimation based on a polar coordinate system of a defective chip; and
performing unsupervised learning for generating a defect pattern clustering model using the feature of the denoised binarized inspection data.