US-20260126730-A1 - PROCESS PROXIMITY CORRECTION (PPC) METHOD BASED ON DEEP LEARNING, AND SEMICONDUCTOR MANUFACTURING METHOD COMPRISING THE PPC METHOD

Abstract

A deep learning-based Process Proximity Correction (PPC) method is provided. The method includes: receiving a first layout of After Clean Inspection (ACI) including a plurality of patterns; extracting a plurality of features of unique patterns from the first layout and sampling the plurality of features of unique patterns; executing an algorithm that optimizes a number of unique patterns sampled in the sampling; identifying optimization conditions and dedose conditions derived from the algorithm; creating a deep learning model based on the optimization conditions and the dedose conditions; and performing correction based on the deep learning model.

Inventors

• Sangchul Yeo
• Chulho Jung
• Jaewon Yang

Assignees

• SAMSUNG ELECTRONICS CO., LTD

Dates

Publication Date

20260507

Application Date

20250502

Priority Date

20240926

Claims (20)

1. 1 . A deep learning-based Process Proximity Correction (PPC) method comprising: receiving a first layout of After Clean Inspection (ACI) comprising a plurality of patterns; extracting a plurality of features of unique patterns from the first layout and sampling the plurality of features of unique patterns; executing an algorithm that optimizes a number of unique patterns sampled in the sampling; identifying optimization conditions and dedose conditions derived from the algorithm; creating a deep learning model based on the optimization conditions and the dedose conditions; and performing correction based on the deep learning model.
2. 2 . The deep learning-based PPC method of claim 1 , wherein the executing of the algorithm comprises analyzing effectiveness of the plurality of features of unique patterns, based on an ensemble model.
3. 3 . The deep learning-based PPC method of claim 2 , wherein the executing of the algorithm comprises: selecting an effective feature comprising a plurality of image parameters; and performing dimensionality reduction.
4. 4 . The deep learning-based PPC method of claim 3 , wherein the is executed based on at least one multi-weight.
5. 5 . The deep learning-based PPC method of claim 4 , wherein the at least one multi-weight is based on Principal Component Analysis (PCA) and Locally Linear Embedding (LLE).
6. 6 . The deep learning-based PPC method of claim 4 , wherein the executing of the algorithm further comprises: calculating Principal Component Analysis (PCA) for features, which are selected in the selecting of the effective feature and the performing of the dimensionality reduction; and extracting principal components.
7. 7 . The deep learning-based PPC method of claim 6 , wherein the principal components extracted after the calculating of the PCA comprise at least two principal components.
8. 8 . The deep learning-based PPC method of claim 6 , wherein the executing of the algorithm comprises: calculating Locally Linear Embedding (LLE) for features, which are selected in the selecting of the effective feature and the performing of the dimensionality reduction; extracting LLE features; and sorting the LLE features in descending order.
9. 9 . The deep learning-based PPC method of claim 8 , wherein the executing of the algorithm comprises performing binning by dividing a uniform space based on the calculated PCA features, and wherein a number of dimensions in the uniform space corresponds to a number of PCA features.
10. 10 . The deep learning-based PPC method of claim 9 , wherein the executing of the algorithm comprises performing sampling on samples in the uniform space, based on the extracted LLE features.
11. 11 . A method of manufacturing a semiconductor device, the method comprising: receiving a first layout of After Clean Inspection (ACI) that comprises a plurality of patterns; generating a second layout of After Development Inspection (ADI) by performing a deep learning-based Process Proximity Correction (PPC) method on the first layout; and generating a third layout by performing Optical Proximity Correction (OPC) method on the second layout.
12. 12 . The method of claim 11 , wherein the generating of the second layout comprises: extracting a plurality of features of unique patterns from the first layout and sampling the plurality of features of unique patterns; executing a first algorithm that optimizes a number of unique patterns that are sampled; identifying optimization conditions and dedose conditions derived from the first algorithm; creating a deep learning model based on the optimization conditions and the dedose conditions that are identified; and performing correction based on the deep learning model.
13. 13 . The method of claim 12 , wherein the executing of the first algorithm comprises: analyzing feature effectiveness of the unique patterns, based on an ensemble model; selecting an effective feature that comprises a plurality of image parameters; performing dimensionality reduction; and executing a second algorithm that considers at least one multi-weight.
14. 14 . The method of claim 13 , wherein the plurality of image parameters comprise at least one of Image Log Slope (ILS), Normalized Image Log Slope (NILS), and Mask Error Enhancement Factor (MEEF).
15. 15 . The method of claim 13 , wherein the at least one multi-weight is based on Principal Component Analysis (PCA) and Locally Linear Embedding (LLE).
16. 16 . The method of claim 13 , wherein the executing of the second algorithm comprises: for features selected in the selecting of the effective feature comprising the plurality of image parameters and the performing of the dimensionality reduction, extracting principal components by calculating Principal Component Analysis (PCA); calculating Locally Linear Embedding (LLE); extracting LLE features; and sorting the LLE features in descending order, and wherein at least two principal components are extracted after the calculating of the PCA.
17. 17 . The method of claim 16 , wherein the executing of the second algorithm comprises: performing binning by dividing a uniform space based on the calculated PCA features to obtain a binned uniform space; and performing sampling on samples in the binned uniform space, based on the extracted LLE features.
18. 18 . The method of claim 17 , wherein, based on samples being in the binned uniform space, one LLE feature-based sample exists for each uniform space.
19. 19 . A method of manufacturing a mask, the method comprising: receiving a first layout comprising patterns; generating a second layout by performing a deep learning-based Process Proximity Correction (PPC) method on the first layout; generating a third layout by performing Optical Proximity Correction (OPC) on the second layout; transmitting mask tape-out (MTO) design data indicating the third layout; preparing mask data based on the MTO design data; and exposing a mask substrate to light based on the mask data.
20. 20 . The method of claim 19 , wherein the generating of the second layout comprises: extracting a plurality of features of unique patterns from the first layout; sampling the plurality of features of unique patterns; executing a first algorithm that optimizes a number of unique patterns that are sampled; identifying optimization conditions and dedose conditions derived from the first algorithm; building a deep learning model based on the optimization conditions and the dedose conditions; and performing correction based on the deep learning model, wherein the executing of the first algorithm comprises: analyzing feature effectiveness of the unique patterns, based on an ensemble model; selecting an effective feature that comprises a plurality of image parameters; performing dimensionality reduction; and executing a second algorithm that considers multi-weights, based on PCA and LLE.

Description

CROSS-REFERENCE TO RELATED APPLICATION This application claims priority to Korean Patent Application No. 10-2024-0131073, filed on Sep. 26, 2024, in the Korean Intellectual Property Office, the disclosure of which is incorporated by reference herein in its entirety. BACKGROUND The present disclosure relates to a method of manufacturing a semiconductor device, and more particularly, to a process proximity correction (PPC) method and a method of manufacturing a semiconductor device that includes the PPC method. In semiconductor processes, a photolithography process using a mask may be performed to form patterns on a semiconductor substrate such as a wafer. A mask may be defined as a pattern transfer body in which a pattern shape including an opaque material is formed on a transparent base layer material. In mask manufacturing processes, a required circuit and a layout of the circuit are designed. Design data obtained through Optical Proximity Correction (OPC) may be transmitted as Mask Tape-Out (MTO) design data. Mask Data Preparation (MDP) may be performed based on the MTO design data, and an exposure process and other processes may be carried out on a mask substrate. SUMMARY One or more embodiments provide a deep learning-based Process Proximity Correction (PPC) method having improved reliability and increased process margin, and a method of manufacturing a semiconductor device that includes the PPC method. Technical problems to be solved by the inventive concept are not limited to the above description, and other technical problems may be clearly understood by one of ordinary skill in the art from the descriptions provided hereinafter. According to an aspect of an embodiment, a deep learning-based PPC method includes: receiving a first layout of After Clean Inspection (ACI) including a plurality of patterns; extracting a plurality of features of unique patterns from the first layout and sampling the plurality of features of unique patterns; executing an algorithm that optimizes a number of unique patterns sampled in the sampling; identifying optimization conditions and dedose conditions derived from the algorithm; creating a deep learning model based on the optimization conditions and the dedose conditions; and performing correction based on the deep learning model. According to another aspect of an embodiment, a method of manufacturing a semiconductor device, includes: receiving a first layout of ACI that includes a plurality of patterns; generating a second layout of After Development Inspection (ADI) by performing a deep learning-based PPC method on the first layout; and generating a third layout by performing Optical Proximity Correction (OPC) method on the second layout. According to another aspect of an embodiment, a method of manufacturing a mask, includes: receiving a first layout including patterns; generating a second layout by performing a deep learning-based PPC method on the first layout; generating a third layout by performing OPC on the second layout; transmitting mask tape-out (MTO) design data indicating the third layout; preparing mask data based on the MTO design data; and exposing a mask substrate to light based on the mask data. BRIEF DESCRIPTION OF DRAWINGS The above and other aspects and features will be more apparent from the following description of embodiments, taken in conjunction with the accompanying drawings, in which: FIG. 1 is a flowchart schematically showing operations of a method of manufacturing a semiconductor device, according to an embodiment; FIG. 2 is a detailed flowchart of a deep learning-based Process Proximity Correction (PPC) method included in a method of manufacturing a semiconductor device, according to an embodiment; FIG. 3 is a detailed flowchart of a sample number optimization algorithm included in a method of manufacturing a semiconductor device, according to an embodiment; FIG. 4 is a detailed flowchart of an active sampling algorithm that considers multi-weights, which is included in a method of manufacturing a semiconductor device according to an embodiment; FIG. 5 is a schematic conceptual view of an operation of analyzing feature effectiveness based on an ensemble model, which is included in a method of manufacturing a semiconductor device according to an embodiment; FIG. 6 is a schematic conceptual view of an operation of calculating Principal Component Analysis (PCA) and extracting principal components, which is included in a method of manufacturing a semiconductor device according to an embodiment; FIG. 7 is a schematic conceptual view of an operation of calculating Locally Linear Embedding (LLE) and extracting LLE features, which is included in a method of manufacturing a semiconductor device according to an embodiment; FIG. 8 is a schematic conceptual view of operations of calculating LLE, arranging LLE features in descending order, and sorting the LLE features, which are included in a method of manufacturing a semiconductor device according to