CMP Spectral Clustering for Accurate Polishing Endpoint Detection
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Solution Overview
Problem
Chemical mechanical polishing (CMP) processes face challenges in determining the polishing endpoint due to variations in material removal rates caused by initial thickness, slurry composition, polishing pad conditions, and load on the substrate, making it difficult to determine when a substrate layer has been planarized to the desired flatness or thickness.
Innovation Solution
A computer-based method that receives and analyzes spectra from a substrate using a clustering algorithm to separate measured spectra into groups based on spectral characteristics, allowing for the determination of characterizing values such as layer thickness, which helps in accurately determining the polishing endpoint.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If optical monitoring is performed in a stand-alone metrology station, then measurement accuracy is improved, but throughput is reduced
Solution Approach 1:
The patent combines the metrology station with the CMP apparatus by integrating the optical monitoring system directly into the polishing process. The controller coordinates both the CMP operation and optical measurements, allowing simultaneous polishing and monitoring without removing substrates to separate stations, thereby maintaining measurement accuracy while improving throughput.
Solution Approach 2:
The system performs preliminary optical monitoring during the polishing process itself rather than after completion. By measuring spectral data in-situ during polishing, the system can detect endpoint conditions earlier and more accurately, eliminating the need for post-polishing verification and reducing total processing time.
2Reliability
If spectral data is collected from multiple positions on the substrate, then measurement comprehensiveness is improved, but data complexity increases
Solution Approach 1:
The patent segments the spectral data from multiple substrate positions into distinct groups using clustering algorithms. The controller separates spectra based on similarity, creating manageable groups that represent different regions or conditions on the substrate. This segmentation reduces data complexity by organizing raw spectral data into structured, interpretable clusters while maintaining comprehensive coverage of all measured positions.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach improves the precision of endpoint detection in CMP processes by accurately characterizing structural features and measuring layer thickness, enhancing the reliability of endpoint determination.
Implementation Method 1
receiving, by one or more computers, a plurality of measured spectra reflected from a substrate
Data Source
AI summary
Among other things, a computer-based method is described. The method comprises receiving, by one or more computers, a plurality of measured spectra reflected from a substrate at a plurality of different positions on the substrate. The substrate comprises at least two regions having different structural features. The method also comprises performing, by the one or more computers, a clustering algorithm on the plurality of measured spectra to separate the plurality of measured spectra into a number of groups based on the spectral characteristics of the plurality of measured spectra; selecting one of the number of groups to provide a selected group having a subset of spectra from the plurality of measured spectra; and determining, in the one or more computers, at least one characterizing value for the substrate based on the subset of spectra of the selected group.