Semiconductor Yield Management with Automated Data Cleaning
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Solution Overview
Problem
Conventional semiconductor yield management systems are limited by manual processing, susceptibility to human error, inability to handle both continuous and categorical variables, missing data, and inability to rapidly identify key yield factors, leading to inefficient analysis and inaccurate models.
Innovation Solution
A fully automated yield management system that processes both continuous and categorical variables, handles missing data, and identifies multiple key yield factors simultaneously, using advanced splitting rules and user-controlled model building to generate interpretable decision trees.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If conventional yield management systems use manual processing, then the system is easier to implement, but the analysis speed is slow and the system is susceptible to human error
Solution Approach 1:
The system automatically processes yield data through automated data cleaning, outlier filtering, and model generation without requiring manual intervention. The automated system performs data validation, handles missing values, and generates yield predictions independently, eliminating human error while maintaining implementation feasibility through a standardized automated workflow.
2Productivity
If conventional systems process each yield parameter separately, then the processing is simpler, but the analysis is time-consuming and cannot identify multiple parameters simultaneously
Solution Approach 1:
The system merges multiple yield parameter processing operations into a single integrated analysis workflow. Instead of processing parameters sequentially, the system simultaneously evaluates multiple prediction variables and identifies multiple key yield factors in one pass through the data, dramatically improving analysis speed while using sophisticated algorithms to manage the increased processing complexity.
3Reliability
If conventional systems remove data sets with missing values, then the data quality is improved, but usable data containing significant prediction variables is lost
Solution Approach 1:
The system extracts and removes only the specific missing values from the data set rather than discarding entire data sets. The automated data cleaning process identifies and handles missing values for individual prediction variables while preserving the rest of the data, including data sets that contain significant prediction variables. This approach maintains data quality by addressing missingness at the variable level while preserving maximum data volume for analysis.
4Measurement precision
If conventional systems use basic splitting rules, then the model generation is faster, but the model accuracy is limited and cannot accommodate user knowledge
Solution Approach 1:
The system uses dynamic splitting rules that can adapt based on user input and data characteristics. The model builder incorporates user-specified weights and priorities for different prediction variables, allowing the splitting process to dynamically adjust to user knowledge and requirements. This dynamic approach improves model accuracy by incorporating domain expertise while managing model building time through efficient algorithmic implementation.
Data Source
AI summary
A system and method for yield management are disclosed wherein a data set containing one or more prediction variable values and one or more response variable values is input into the system. The system can process the input data set to remove prediction variables with missing values and data sets with missing values based on a tiered splitting method to maximize usage of all valid data points. The processed data can then be used to generate a model that may be a decision tree. The system can accept user input to modify the generated model. Once the model is complete, one or more statistical analysis tools can be used to analyze the data and generate a list of the key yield factors for the particular data set.