Automated Semiconductor Data Analysis System
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Solution Overview
Problem
Current solutions for data analysis in semiconductor manufacturing fabrication are time-consuming, prone to configuration issues, and result in data latency, making it difficult to detect process variations and ensure consistency across multiple fabrication plants, with limited user control over batch jobs and ad-hoc reporting.
Innovation Solution
An automated analysis system that includes a data analysis engine with modular and scalable components for calculating statistical indicators, providing automated configuration synchronization, reduced data latency, and user-controlled batch job scheduling, enabling fast and consistent data analysis across multiple fabrication plants.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If home-grown applications and analysis scripts are utilized for data analysis, then customization flexibility is improved, but configuration time and complexity increase significantly
Solution Approach 1:
The system segments the analysis configuration into reusable templates and parameter sets. Each analysis type has a predefined template structure that can be independently configured and reused, eliminating the need to recreate analysis configurations from scratch and significantly reducing configuration time while maintaining customization flexibility.
Solution Approach 2:
The system allows dynamic parameter changes within predefined templates. Users can modify parameters such as statistical calculations, chart types, and data sources without changing the underlying template structure, enabling flexible customization with minimal configuration effort and time.
2Adaptability or versatility
If manual data manipulation and analysis is performed, then analysis flexibility is improved, but productivity and response time deteriorate
Solution Approach 1:
The system performs preliminary actions by pre-defining analysis templates, calculation algorithms, and data processing logic before actual analysis is needed. When an analysis is required, the system retrieves the appropriate template and applies it to the data, eliminating manual data manipulation and significantly reducing response time while maintaining analysis flexibility through template selection and parameter configuration.
3Reliability
If centralized data aggregation is implemented, then consistency across multiple fabs is improved, but data latency increases
Solution Approach 1:
The system implements dynamic data aggregation with configurable frequencies and triggers. Instead of static centralized aggregation, the system can aggregate data in real-time, near-real-time, or on-demand based on the specific analysis requirements. This dynamic approach maintains consistency across multiple fabs while minimizing data latency by aggregating only when necessary and at optimal intervals.
4Measurement precision
If complex data processing algorithms are implemented, then analysis precision is improved, but system complexity and difficulty of integration increase
Solution Approach 1:
The system implements a universal algorithm framework that provides standardized interfaces for various statistical and analytical algorithms. The same framework supports multiple algorithms (mean, standard deviation, control limits, capability indices) through a consistent API, enabling high-precision analysis while reducing system complexity through uniform integration mechanisms and eliminating the need for separate integration code for each algorithm.
Data Source
AI summary
A system and method for an automated analysis system for semiconductor manufacturing fabrication is disclosed. The system includes one or more site databases that each store data generated by an associated one or more semiconductor fabrication plants, a configuration database, and a server communicatively coupled to the one or more site databases and the configuration database, the server to analyze the data from the one or more site databases upon a request by a client, the data to be analyzed based on configuration settings in the configuration database that provide uniform configuration synchronization for applying algorithms to the data.