Semiconductor Defect Inspection Using DDR-Based Classification
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Solution Overview
Problem
Defect inspection in semiconductor devices is time-consuming and costly, necessitating more efficient methods and systems.
Innovation Solution
A system and method utilizing a C-mode scanning acoustic microscope, optical sensors, and image processing to detect defects in semiconductor wafers, including hybrid bonding techniques and optical signal analysis to identify and classify defects, with threshold criteria for scrapping or shipping devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If image processing is used for defect inspection, then measurement precision is improved, but loss of time increases
Solution Approach 1:
The patent performs preliminary actions by calculating defect die ratios and determining scrapping thresholds before actual defect inspection. Defects are classified into categories (first category defects requiring scrapping, second category defects allowing shipping) based on pre-established criteria. This preliminary classification enables faster decision-making during inspection by eliminating the need for complex post-inspection analysis, thus reducing inspection time while maintaining detection precision.
Solution Approach 2:
The patent changes parameters by establishing specific defect die ratio thresholds and classification criteria that define when a semiconductor device should be scrapped versus shipped. By converting complex defect assessment into simple parameter comparisons (defect die ratio vs. threshold), the system achieves rapid defect evaluation without sacrificing measurement precision, directly resolving the time-precision contradiction.
2Reliability
If comprehensive defect inspection is performed, then reliability is improved, but loss of time increases
Solution Approach 1:
The patent segments defect inspection into distinct categories: first category defects (requiring scrapping) and second category defects (allowing shipping). This segmentation is based on pre-calculated defect die ratios and established thresholds. By dividing comprehensive inspection into targeted category-specific assessments, the system maintains reliability through thorough evaluation while reducing time through efficient classification and decision-making.
Solution Approach 2:
The patent extracts and separates critical quality decisions from the overall inspection process by identifying and classifying defects into distinct categories with predetermined outcomes. This extraction allows the system to focus inspection efforts on critical defects that determine scrapping decisions, maintaining reliability while eliminating time spent on exhaustive analysis of all defect types.
3Productivity
If defect detection is performed early, then productivity is improved, but device complexity increases
Solution Approach 1:
The patent applies universality by using a single integrated system that performs multiple functions: acquiring defect information, calculating defect die ratios, classifying defects into categories, determining scrapping decisions, and providing feedback. This multi-functional approach enables early defect detection with improved productivity while avoiding the need for multiple separate complex systems, thus managing device complexity through functional integration.
Solution Approach 2:
The patent implements feedback mechanisms where defect detection results immediately inform scrapping decisions and quality assurance outcomes. The system provides real-time feedback by comparing defect die ratios against thresholds and automatically determining whether devices should be scrapped or shipped. This feedback loop enables early detection to translate directly into productivity improvements without requiring complex manual intervention processes.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enhances defect detection precision and efficiency, reducing costs by identifying defects before final quality assurance stages and improving semiconductor device quality.
Implementation Method 1
utilizing a C-mode scanning acoustic microscope
Implementation Method 2
optical sensors, and image processing to detect defects
Data Source
AI summary
A method and a system for detecting a semiconductor device are provided. The method comprises obtaining an image of the semiconductor device, evaluating a feature of the image, detecting a defect of the semiconductor device based on the feature, extracting a defect information for the defect, calculating a defect die ratio (DDR) in response to the defect and analyzing a relation between the DDR and the defect information.