A MACHINE LEARNING MODEL USING TARGET PATTERN AND REFERENCE LAYER PATTERN TO DETERMINE OPTICAL PROXIMITY CORRECTION FOR MASK

• Main Authors: BOONE, Robert Elliott, Fong, Wei-chun, ZHANG, Quan, ZHU, Zhangnan, CHEN, Been-Der
• Format: Patent
• Language: English
• Published: 11.04.2024
• Subjects: APPARATUS SPECIALLY ADAPTED THEREFOR; CALCULATING; CINEMATOGRAPHY; COMPUTING; COUNTING; ELECTROGRAPHY; HOLOGRAPHY; IMAGE DATA PROCESSING OR GENERATION, IN GENERAL; MATERIALS THEREFOR; ORIGINALS THEREFOR; PHOTOGRAPHY; PHOTOMECHANICAL PRODUCTION OF TEXTURED OR PATTERNED SURFACES,e.g. FOR PRINTING, FOR PROCESSING OF SEMICONDUCTORDEVICES; PHYSICS

Described are embodiments for generating a post-optical proximity correction (OPC) result for a mask using a target pattern and reference layer patterns. Images of the target pattern and reference layers are provided as an input to a machine learning (ML) model to generate a post-OPC image. The images may be input separately or combined into a composite image (e.g., using a linear function) and input to the ML model. The images are rendered from pattern data. For example, a target pattern image is rendered from a target pattern to be printed on a substrate, and a reference layer image such as dummy pattern image is rendered from dummy pattern. The ML model is trained to generate the post-OPC image using multiple images associated with target patterns and reference layers, and using a reference post-OPC image of the target pattern. The post-OPC image may be used to generate a post-OPC mask.