Processing Station Fingerprinting for Semiconductor Thread Analysis
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Solution Overview
Problem
In semiconductor manufacturing, it is challenging to accurately characterize post-processing data in terms of individual contributions from processing stations due to the complexity of multiple patterning processes and the large number of potential combinations of processing tools and stations, making it difficult to establish reliable models linking pre-processing and post-processing data.
Innovation Solution
A method is introduced to characterize post-processing data by obtaining and analyzing data in a cyclic sequence of processing threads, determining individual contributions from processing stations by comparing subsets of post-processing data from substrates with shared process sub-threads, and performing hierarchical fingerprint decomposition to isolate individual tool contributions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If comprehensive pre-processing data from multiple processing stations is collected to improve prediction accuracy, then the reliability of post-processing prediction improves, but the device complexity and data processing burden increase significantly
Solution Approach 1:
The patent segments the complex manufacturing process into distinct process threads, where each thread represents a specific sequence of processing stations. By grouping substrates with identical process threads together, the system divides the large-scale data processing problem into smaller, manageable segments that can be analyzed independently, reducing overall computational complexity while maintaining prediction reliability.
Solution Approach 2:
The patent transforms the prediction approach by changing parameters from analyzing individual substrate data points to analyzing aggregated process thread characteristics. This parameter transformation allows the system to work with summarized statistics (mean, standard deviation) of process threads rather than raw individual substrate measurements, significantly reducing data dimensionality while preserving essential information for reliable prediction.
2Manufacturing precision
If detailed analysis of all processing station contributions is performed to improve manufacturing precision, then the accuracy of error correction improves, but the measurement effort and time required increase
Solution Approach 1:
The patent applies partial action by focusing metrology efforts only on substrates with non-default process threads rather than analyzing every substrate in detail. By identifying and prioritizing only the substrates that deviate from the standard process flow, the system achieves sufficient correction accuracy for the most critical cases while avoiding unnecessary detailed analysis of routine substrates, thus reducing overall measurement time and effort.
Data Source
AI summary
A method for characterizing post-processing data in terms of individual contributions from processing stations, the post-processing data relating to a manufacturing process for manufacturing integrated circuits on a plurality of substrates using a corresponding processing apparatus for each of a plurality of process steps, at least some of the processing apparatuses each including a plurality of the processing stations, and wherein the combination of processing stations used to process each substrate defines a process thread for the substrate; the method including: obtaining post-processing data associated with processing of the plurality of substrates in a cyclic sequence of processing threads; and determining an individual contribution of a particular processing station by comparing a subset of the post-processing data corresponding to substrates having shared process sub-threads, wherein a process sub-thread describes the process steps of each process thread other than the process step to which the particular processing station corresponds.