Wafer Analysis Using Artificial Neural Network
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Solution Overview
Problem
Traditional wafer analysis methods are time-consuming and labor-intensive, relying on human experience, which can lead to incorrect judgments and increased costs due to the risk of human error.
Innovation Solution
An artificial neural network system integrated with JAVA software is developed to monitor and analyze wafer test results, utilizing test data to identify abnormal conditions and train the system, reducing the need for human intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional human operator analysis is used for wafer quality monitoring, then experience-based judgment can be applied, but it wastes too much time and manpower and increases the risk of wrong judgment
Solution Approach 1:
The patent replaces the mechanical system of human operator analysis with an automated computer-based analysis system. The system uses software algorithms to automatically analyze wafer test data, replacing the manual examination process. This substitution eliminates the time consumption and subjectivity of human analysis while maintaining or improving accuracy through consistent algorithmic evaluation.
Solution Approach 2:
The analysis system is designed to autonomously process wafer test data without requiring continuous human intervention. The system automatically monitors test results, identifies abnormal conditions, and generates analysis reports independently. This self-service capability reduces manpower requirements and accelerates the analysis process while maintaining reliable results through programmed evaluation criteria.
2Reliability
If human operators analyze wafer test results, then judgment can be made based on experience, but experience deficiency and wrong judgment would cause more waste of time and manpower and increase cost
Solution Approach 1:
The patent replaces the mechanical system of human operator analysis with an automated computer-based analysis system. The system uses software algorithms to automatically analyze wafer test data, replacing the manual examination process. This substitution eliminates the time consumption and subjectivity of human analysis while maintaining or improving accuracy through consistent algorithmic evaluation.
Solution Approach 2:
The system transforms the analysis process from subjective human judgment to objective parameter-based evaluation. By converting qualitative experience-based decisions into quantitative algorithmic parameters, the system achieves consistent and reproducible results. The analysis criteria are defined through specific parameters and thresholds that can be objectively applied to all wafer samples, eliminating variability in human judgment.
3Productivity
If automated system is used for wafer analysis, then time and manpower are saved, but the system requires training with cumulative experiences to improve accuracy
Solution Approach 1:
The system incorporates a training phase that occurs before full operational deployment. During this preliminary action phase, the system is trained using historical wafer test data and known outcomes to establish accurate analysis criteria. This pre-training ensures that when the system enters production use, it already possesses the necessary knowledge to accurately identify abnormal conditions, eliminating the need for complex ongoing training adjustments.
Solution Approach 2:
The system implements a feedback mechanism where analysis results and actual wafer outcomes are continuously fed back into the training database. This feedback loop allows the system to learn from accumulated experience and continuously improve its analysis accuracy. The feedback process automatically adjusts analysis parameters and thresholds based on real-world performance, simplifying the overall system complexity over time as the system becomes more refined.
Data Source
AI summary
A method for wafer analysis with artificial neural network and the system thereof are disclosed. The method of the system of the present invention has several steps, including: first of all, providing a test unit for wafer test and generating a plurality of test data; next, transmitting the test data to a processing unit for transferring to output data; then, comparing the output data with predictive value and modifying bias and making the output data close to the predictive value, and repeating the steps mentioned above to train this system; finally, analyzing wafers by the trained system. Using this system to analyze wafers not only saves time, but also reduces manpower and the risk resulting from artificial analysis.