AI Process Condition Modeling for Semiconductor Yield Uniformity
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Solution Overview
Problem
In the semiconductor fabrication field, improving yield and achieving uniformity in semiconductor devices is challenging due to the complexity of process parameters and the difficulty in analyzing massive data generated from automated equipment, which restricts the use of simple statistical analysis and empirical research.
Innovation Solution
A method is developed to predict an optimal process condition model by collecting process parameter information, calculating process global uniformity, creating a data set, and using artificial intelligence to create a virtual process environment function, which enables the creation of an optimal process condition model for improving yield and controlling semiconductor fabrication processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If simple statistical analysis or empirical research is used for yield improvement, then the analysis process is simple and easy to implement, but the accuracy of analyzing process factors is insufficient due to the complexity of process parameters
Solution Approach 1:
The patent replaces traditional statistical analysis methods with artificial intelligence technology. The AI system automatically analyzes massive process data from semiconductor fabrication equipment, identifying complex relationships between process parameters and yield without requiring manual statistical methods. This substitution enables accurate analysis of numerous process factors while maintaining ease of implementation through automated processing.
Solution Approach 2:
The patent transforms process data into structured formats suitable for AI analysis. By converting raw process parameters into standardized data structures and using AI algorithms to process these transformed parameters, the system achieves accurate yield factor identification without the limitations of traditional statistical methods.
2Quantity of substance
If traditional statistical analysis is used to analyze massive data from automated equipment, then the data volume is large, but the analysis accuracy deteriorates due to the increased amount of data and factors
Solution Approach 1:
The patent replaces traditional statistical analysis systems with AI-based analysis systems capable of handling massive data volumes. The AI technology processes large amounts of process data from automated equipment, maintaining analysis accuracy even as data quantity increases, thereby resolving the limitation of traditional methods when faced with big data.
Solution Approach 2:
The patent implements a dynamic analysis system that adapts to varying data volumes and complexity. The AI system dynamically adjusts its processing capabilities to handle different amounts of process data while maintaining consistent analysis accuracy, enabling effective utilization of massive data without the degradation seen in static statistical methods.
3Productivity
If the number of semiconductor chips manufactured from one wafer is greatly increased, then the productivity is improved, but the uniformity of characteristic values becomes more difficult to control
Solution Approach 1:
The patent implements feedback mechanisms where AI analysis of process data provides insights into variations in characteristic values across multiple chips on a wafer. This feedback enables real-time adjustments to process parameters, maintaining uniformity even as the number of chips per wafer increases, thereby resolving the trade-off between productivity and precision.
Solution Approach 2:
The patent uses AI-driven parameter optimization to adjust process conditions based on analyzed data patterns. By dynamically changing process parameters based on AI insights, the system maintains characteristic value uniformity across increased numbers of chips per wafer, enabling higher productivity without sacrificing manufacturing precision.
Data Source
AI summary
In a method of predicting an optimal process condition model for a semiconductor fabrication process, process parameter information of a unit process in the semiconductor fabrication process may be collected. First characteristics information of objects to be processed before the unit process and second characteristic information of processed objects after the unit process may be extracted. Process global uniformity (PGU) may be calculated using the first characteristic information and the second characteristic information. A data set of the unit process may be created using the process parameter information and the PGU. A virtual process environment function of the unit process may be created using the data set. The optimal process condition model of the unit process may be created using the virtual process environment function.