Automated Model Builder for Semiconductor Process Tools
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Solution Overview
Problem
The semiconductor manufacturing process faces challenges in efficiently creating and updating models for process tools, as current methods are labor-intensive, prone to human error, and require significant expertise, leading to inconsistencies and increased time for model creation and updating.
Innovation Solution
An automated system and process for creating and updating models using statistical analysis and mathematical principles, where data from the manufacturing process triggers a template to generate or update models, reducing the need for manual input from process engineers and improving consistency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If manual model creation and updating is performed by process engineers, then model accuracy and expertise-based parameter specification are improved, but time consumption and labor intensity increase significantly
Solution Approach 1:
The system enables automated model creation and updating through self-service mechanisms. The automated model builder continuously monitors process data, performs statistical analysis, and updates models without human intervention. This resolves the contradiction by eliminating manual labor while maintaining model accuracy through algorithmic parameter specification based on actual process performance.
Solution Approach 2:
The system implements continuous feedback loops where process data is monitored, analyzed, and used to automatically update model parameters. The automated model builder compares actual process output with model predictions and adjusts parameters accordingly. This feedback mechanism maintains model accuracy while eliminating the time-consuming manual update process.
2Manufacturing precision
If manual model creation is performed by process engineers, then expertise-based parameter specification is improved, but consistency and reproducibility deteriorate due to human error
Solution Approach 1:
The automated model builder eliminates human error by using algorithmic processes for parameter specification. The system objectively analyzes process data and determines parameters through statistical methods rather than subjective engineer judgment. This ensures consistent and reproducible model creation across different engineers and time periods while maintaining accuracy.
Solution Approach 2:
The system dynamically adjusts model parameters based on statistical analysis of actual process data rather than fixed manual specifications. This data-driven approach ensures parameters reflect real process behavior and maintains consistency across model updates. The automated adjustment of parameters based on monitored variables improves both accuracy and reliability.
3Measurement precision
If comprehensive process data monitoring and statistical analysis are implemented, then model updating accuracy is improved, but system complexity increases
Solution Approach 1:
The system replaces manual mechanical processes with automated computational systems. Statistical analysis and parameter adjustment that would require complex human expertise are substituted with algorithmic processing of process data. This substitution maintains high measurement precision while managing system complexity through standardized computational methods rather than complex human procedures.
Data Source
AI summary
A system and computer-implemented method for creating a new model or updating a previously-created model based on a template are described. A template is generated from a previously-created model. The previously-created model specifies a set of parameters associated with a manufacturing process, a process tool or chamber. Variables associated with the manufacturing process are acquired, monitored, and analyzed. A statistical analysis (or multivariate statistical analysis) is employed to analyze the monitored variables and the set of parameters. When any of the monitored variables satisfy a threshold condition, a new model is created or the parameters of the previously-created model are updated, adjusted, or modified based on the template and the monitored variables. A user interface facilitating communication between a user and the systems and display of information is also described.