Automated Semiconductor Recipe Generation from Design Data
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Solution Overview
Problem
Conventional manual or semi-manual recipe generation processes in the semiconductor industry are time-consuming and prone to errors due to their reliance on data derived directly from produced wafers, which complicates the inspection, metrology, and review processes.
Innovation Solution
A method and system that utilize design data to identify repetitive areas and generate recipes for manufacturing tools, allowing for automated decision-making on inspection approaches such as cell-to-cell or die-to-die inspection without relying on data from produced wafers, thereby reducing processing time and resource usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual or semi-manual recipe generation processes are used, then reliability may be maintained through human review, but time consumption and error rates increase significantly
Solution Approach 1:
The system performs preliminary actions by automatically generating recipe parameters from design data before actual manufacturing begins. The computer extracts process parameters, inspection parameters, and metrology parameters directly from the design data, creating a complete recipe specification in advance that eliminates the need for time-consuming manual recipe creation after wafer production.
Solution Approach 2:
The system creates a digital copy of the design data and extracts recipe parameters from this copy without needing physical wafer samples. The computer processes the design data to generate all necessary recipe parameters, replacing the conventional approach that requires capturing images of actual produced wafers and manually transcribing data.
2Reliability
If data from produced wafers is used for recipe generation, then real-world manufacturing conditions are captured, but processing time and resource usage increase
Solution Approach 1:
The system performs preliminary extraction of all recipe parameters from design data before wafer production. The computer identifies process parameters, inspection parameters, and metrology parameters in advance from the design specifications, eliminating the need to wait for wafer production and manually extract data from physical samples.
Solution Approach 2:
The computer acts as an intermediary that directly translates design data into recipe parameters without requiring physical wafer intermediaries. The system processes design data to generate process parameters, inspection parameters, and metrology parameters, replacing the conventional chain that requires actual wafer production, image capture, and manual data extraction.
3Adaptability or versatility
If conventional manual processes are used, then flexibility in handling unique cases is maintained, but automation and consistency are reduced
Solution Approach 1:
The computer system performs multiple functions automatically: extracting process parameters, inspection parameters, and metrology parameters from design data, and generating complete recipes for various manufacturing tools. This universal automated system replaces multiple manual processes while maintaining adaptability through programmable parameter extraction rules.
Solution Approach 2:
The system enables self-service by automatically generating recipes from design data without requiring manual intervention. The computer processes design data, extracts necessary parameters, and creates complete recipe specifications autonomously, eliminating the need for human operators to manually create recipes while maintaining consistency and accuracy.
Data Source
AI summary
A computer creates a recipe for a manufacturing tool based on design data. The computer obtains the design data, which includes basic elements and hierarchical levels corresponding to the basic elements. The computer selects one or more basic elements of interest and generates one or more sets of simple array cells corresponding to a level of interest. The computer uses the sets of simple array cells to identify periodical areas in level-of-interest coordinates to enable automated recipe creation. The periodical areas are identified with respect to one or more basic elements.