Solder Temperature Profiling for Internal Defect Detection
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Solution Overview
Problem
Current soldering quality control methods, such as ICT, X-ray, and AOI tests, are limited in detecting defects like empty solder, cold welding, and tin explosion, as they rely on expensive equipment or surface inspections that cannot effectively assess internal solder joint quality.
Innovation Solution
A soldering process method that measures temperature profiles during soldering using machine learning to establish optimal and classified temperature profile models, enabling the detection of defects like tin explosion, empty solder, and cold welding by analyzing temperature changes throughout the soldering process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional inspection methods (ICT, X-ray, AOI) are used to detect solder defects, then equipment cost is high or detection capability is limited, but defect detection accuracy is insufficient for internal solder joint quality
Solution Approach 1:
The patent replaces complex mechanical inspection systems (X-ray equipment, AOI systems) with a thermal field-based detection method. By measuring temperature distribution and changes during the soldering process, the system can infer internal solder joint quality without requiring expensive penetration-type inspection equipment. This substitutes mechanical/optical inspection with thermal field analysis.
Solution Approach 2:
The patent changes the detection parameter from visual appearance (AOI) or electrical continuity (ICT) to temperature characteristics. By monitoring temperature distribution, heating rate, and cooling rate during soldering, the system can detect internal defects like empty solder and cold welding through thermal anomaly patterns, providing a new parameter dimension for quality assessment.
2Reliability
If surface inspection methods (AOI) are used to check solder joints, then inspection speed is fast, but internal defect detection capability is insufficient
Solution Approach 1:
The patent performs temperature measurement and defect detection during the soldering process itself, rather than as a separate post-inspection step. By continuously monitoring temperature parameters throughout heating, melting, and cooling phases, the system detects internal defects in real-time, eliminating the need for separate inspection operations and maintaining high productivity.
Solution Approach 2:
The temperature measurement system serves multiple functions: it monitors the soldering process parameters, detects internal solder joint defects, and provides quality classification all in one integrated system. This multi-functionality allows the same equipment to perform both process control and quality inspection, improving reliability without sacrificing productivity.
3Measurement precision
If machine learning analysis of temperature profiles is implemented, then defect prediction accuracy improves, but data processing complexity increases
Solution Approach 1:
The patent focuses on analyzing specific critical temperature parameters (heating rate, peak temperature, cooling rate) rather than processing all possible temperature data points. By concentrating on the most informative partial data set that directly correlates with solder joint quality, the system achieves high prediction accuracy while keeping data processing complexity manageable.
Solution Approach 2:
The machine learning model uses feedback from measured temperature profiles to continuously improve defect prediction accuracy. The system learns from historical temperature data and corresponding defect outcomes, refining its prediction capabilities over time. This feedback mechanism allows the system to achieve high precision without requiring excessively complex processing algorithms.
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
This method improves solder quality control by using temperature profiles to predict and correct defects, reducing the need for costly equipment and enhancing the accuracy of defect detection beyond surface inspections.
Implementation Method 1
using an infrared temperature device to measure the temperature profile of generating the solder structure
Data Source
AI summary
A soldering process method includes the following steps. A temperature profile of generating a solder structure is measured. A final product of the solder structure is tested and recorded. A machine learning method is used to repeatedly compare and analyze a relationship between a plurality of the temperature profiles of the solder structure and a corresponding final product of the solder structure so as to find an optimal temperature profile model in accordance with quality control requirements.