Lithography Process Corrections Using Pre-Exposure Wafer Grouping
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Solution Overview
Problem
Modern lithographic processes face challenges in accurately measuring and correcting overlay and alignment residuals, which are difficult to trace back to their root causes, leading to inefficiencies in run-to-run control strategies and reduced throughput due to the need for extensive measurement operations.
Innovation Solution
A method is introduced that determines process corrections for lithographic processes by grouping substrates based on pre-exposure metrology data, using a classifier trained on post-exposure data to assign group membership status and apply corresponding corrections, thereby optimizing run-to-run control strategies without the need for full overlay measurement on each substrate.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If full overlay measurement is performed on each substrate to take advantage of wafer level control, then manufacturing precision is improved, but productivity deteriorates due to prohibitive time requirements
Solution Approach 1:
The substrate population is segmented into multiple groups based on pre-exposure metrology data characteristics. Each group receives tailored process corrections rather than uniform treatment, enabling precise control while reducing measurement requirements. The classifier divides substrates into distinct categories that share similar overlay behavior patterns.
Solution Approach 2:
Pre-exposure metrology measurements are performed before the lithographic exposure process to predict post-exposure overlay performance. This preliminary characterization allows corrections to be predetermined and applied without requiring time-consuming post-exposure measurements on every substrate, thus maintaining precision while improving throughput.
2Manufacturing precision
If extensive measurement operations are performed to accurately measure overlay and alignment residuals, then manufacturing precision is improved, but loss of time increases
Solution Approach 1:
The method extracts and utilizes pre-exposure metrology data that is already available from routine process measurements, rather than requiring additional dedicated overlay measurements. By leveraging existing measurement data taken at earlier process stages, the system obtains correction information without incurring extra measurement time penalties.
Solution Approach 2:
The classifier creates a predictive model that copies the relationship between pre-exposure metrology characteristics and post-exposure overlay performance. This model allows overlay prediction based on earlier measurements, effectively copying the information value of post-exposure measurements without repeating the measurement process.
3Ease of operation
If a classifier is trained to categorize substrates based on pre-exposure metrology data, then device complexity increases, but ease of operation improves by automating correction selection
Solution Approach 1:
The classifier system enables automated self-service correction assignment. The control system automatically categorizes substrates based on their pre-exposure metrology data and assigns appropriate process corrections without requiring manual intervention. This automation improves ease of operation despite the increased complexity of the underlying classification algorithms.
Data Source
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AI summary
Disclosed is a method of determining a correction for a process parameter related to a lithographic process on a substrate and associated apparatuses. The lithographic process comprises a plurality of runs during each one of which a pattern is applied to one or more substrates. The method comprises obtaining pre-exposure metrology data describing a property of the substrate; obtaining post- exposure metrology data comprising one or more measurements of the process parameter having been performed on one or more previously exposed substrates; assigning to the substrate, a group membership status from a one or more groups, based on said pre-exposure metrology data; and determining the correction for the process parameter based on said group membership status and said post-exposure metrology data.