Adaptive Wafer Defect Detection Using Balanced Models
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Solution Overview
Problem
Existing defect detection methods in semiconductor manufacturing face challenges such as unbalanced labeled training data, inefficiencies in image preprocessing, and the need for manual intervention, leading to unreliable predictions and increased production losses due to defective wafers.
Innovation Solution
An electronic device with adaptive operation modes for processing semiconductor wafer images, utilizing a defect prediction model trained with balanced data, and employing knowledge-guided filters for image quality verification and restoration to automatically select and apply appropriate models for defect detection, reducing the need for human intervention and improving prediction accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual intervention is used for defect detection, then prediction accuracy can be improved, but production time is lost and resources are wasted
Solution Approach 1:
The system employs automated defect detection algorithms that independently analyze wafer images without requiring manual inspection. The defect prediction model automatically processes semiconductor wafer images, identifies defects, and generates predictions, enabling the system to serve itself and eliminating the need for human intervention in the detection process.
Solution Approach 2:
The patent replaces manual mechanical inspection with an automated electronic defect prediction system. The system uses computational algorithms and machine learning models to substitute human operators, achieving both high-speed automated processing and maintained detection accuracy through intelligent image analysis.
2Reliability
If defect detection is performed on all wafers, then detection reliability is improved, but production time increases
Solution Approach 1:
The system performs defect detection selectively rather than uniformly on all wafers. By using the defect prediction model to identify high-risk wafers that require detailed inspection, the system applies partial action only where necessary, maintaining detection reliability for critical cases while reducing overall processing time through targeted rather than comprehensive inspection.
Solution Approach 2:
The automated defect prediction system enables rapid processing that skips lengthy manual inspection steps. The system quickly analyzes wafer images and generates defect predictions, allowing production to move forward without the time-consuming delays associated with traditional manual detection methods, thereby maintaining reliability while reducing time loss.
3Measurement precision
If image preprocessing is performed to improve quality, then detection accuracy is improved, but processing complexity increases
Solution Approach 1:
The system performs preliminary image preprocessing operations automatically as part of the defect detection workflow. By pre-processing images to enhance quality, adjust contrast, and optimize formatting before defect analysis, the system prepares data in advance to improve detection accuracy while managing complexity through automated sequential processing steps.
Solution Approach 2:
The defect prediction model dynamically adjusts processing parameters based on input image characteristics. By changing parameters such as image resolution, contrast levels, and analysis thresholds according to the specific properties of each wafer image, the system optimizes detection accuracy for varying image qualities without requiring fixed complex preprocessing for all cases.
4Adaptability or versatility
If multiple operation modes are implemented, then adaptability is improved, but device complexity increases
Solution Approach 1:
The defect prediction system is designed with multiple operation modes that enable it to perform various functions: processing wafer images of different sizes, handling different image qualities, and adapting to various production requirements. This multi-functionality allows a single system to handle diverse scenarios without requiring separate dedicated systems for each case.
Solution Approach 2:
The system dynamically switches between different operation modes based on the characteristics of input images and production requirements. Rather than maintaining fixed complex configurations, the system adapts its processing parameters, model selection, and analysis depth dynamically, simplifying the overall device architecture while maintaining high adaptability to varying conditions.
Data Source
AI summary
A method and device with defect detection are included. In one general aspect, a method performed by an electronic device includes determining, by the electronic device, an operation mode among different operation modes, wherein the electronic device is configured to implement the operation modes for determining wafer defects by processing semiconductor wafer images, and determining, by the electronic device, based on an indication of the determined operation mode, whether a semiconductor image, among the semiconductor wafer images, is defective.