Semiconductor Electrical Property Prediction via Selective Metrology
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Solution Overview
Problem
Current semiconductor fabrication processes face challenges in efficiently monitoring and predicting electrical properties of semiconductor specimens due to the complexity of ultra large-scale integration and the need for precise structural parameter measurement.
Innovation Solution
A system comprising a processor and memory circuitry configured to obtain metrology data, use a model to determine structural parameters affecting electrical properties, and generate a recipe for an examination tool to optimize data acquisition based on these parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If comprehensive metrology data acquisition is performed for all structural parameters, then measurement precision of electrical properties is improved, but loss of time and productivity deteriorate
Solution Approach 1:
The system extracts and identifies only the most relevant structural parameters that have significant impact on electrical properties using impact criterion analysis. Instead of measuring all structural parameters, the system selectively acquires data only for parameters that materially affect the electrical property prediction, thereby reducing measurement time while maintaining prediction accuracy.
Solution Approach 2:
The system applies different data acquisition strategies to different structural parameters based on their impact criteria. Parameters with high impact on electrical properties receive higher measurement priority and more frequent sampling, while parameters with low impact are measured less frequently or omitted entirely, optimizing the overall measurement process.
2Measurement precision
If data acquisition rate for all structural parameters is increased, then prediction accuracy of electrical properties is improved, but productivity and manufacturing throughput deteriorate
Solution Approach 1:
The system extracts the essential subset of structural parameters that drive electrical property variations. By focusing measurement resources on only these critical parameters identified through impact criterion analysis, the system achieves accurate predictions without the productivity penalty of measuring all parameters at high rates.
Solution Approach 2:
The system applies partial action by acquiring data at high rates only for the most critical structural parameters that have the greatest impact on electrical properties, while using lower acquisition rates or skipping measurement for less critical parameters, thus balancing accuracy with throughput.
3Reliability
If comprehensive examination of all structural parameters is performed, then reliability of electrical property prediction is improved, but device complexity and processing requirements worsen
Solution Approach 1:
The system extracts and focuses on the critical subset of structural parameters that have significant impact on electrical properties. This reduction in the number of parameters to be examined simplifies the examination process while maintaining prediction reliability, as the selected parameters are those that most strongly influence the electrical property outcomes.
Solution Approach 2:
The system changes the parameter selection criteria from comprehensive measurement of all structural parameters to selective measurement based on impact criterion analysis. This parameter change in the examination approach reduces process complexity while preserving prediction reliability by focusing on the most influential parameters.
Data Source
AI summary
There is provided a method and a system configured to obtain metrology data Dmetrology informative of a plurality of structural parameters of a semiconductor specimen, obtain a model informative of a relationship between at least some of said structural parameters and one or more electrical properties of the specimen, use the model and Dmetrology to determine, for at least one given electrical property of the specimen, one or more given structural parameters among the plurality of structural parameters, which affect the given electrical property according to an impact criterion, and generate a recipe for an examination tool, wherein the recipe enables a ratio between a first acquisition rate of data informative of the one or more given structural parameters, and a second acquisition rate of data informative of other structural parameters of the plurality of structural parameters, to meet a criterion.