Wire Bond Defect Detection Using Standing-Wave Image Comparison
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Solution Overview
Problem
Existing methods for detecting bonding defects in wire bonding, such as slight wire elevation or incomplete bonding, are time-consuming and reduce productivity.
Innovation Solution
A defect detection device using standing waves generated by ultrasonic wave generators to apply suction forces for deformation detection, combined with image capturing to compare pre- and post-suction states for rapid defect identification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a pulling test is carried out for all bonding positions to detect bonding defects, then detection accuracy is improved, but productivity is significantly reduced
Solution Approach 1:
The patent replaces the mechanical pulling test system with an optical imaging system. Instead of physically pulling wires to detect bonding defects, the system uses a camera to capture images of the bonding area and processes these images to detect defects such as ball lift, stitch lift, and bonding cracks. This substitution eliminates the time-consuming mechanical testing while maintaining detection capability through image analysis.
Solution Approach 2:
The patent creates a visual copy (image) of the bonding area to detect defects. By capturing images of the wire bonding area and analyzing these image copies, the system can identify bonding defects without performing physical pulling tests on each bond. The image processing algorithms detect subtle visual changes that indicate bonding quality issues.
2Device complexity
If traditional visual inspection methods are used to detect bonding defects, then the configuration remains simple, but detection accuracy for subtle defects deteriorates
Solution Approach 1:
The patent replaces traditional visual inspection with automated optical imaging and image processing. Instead of relying on human visual inspection or simple optical systems, the invention uses a camera-based imaging system with automated image processing algorithms that can detect subtle bonding defects such as ball lift, stitch lift, and bonding cracks with high precision while maintaining relatively simple device configuration.
Solution Approach 2:
The patent changes the parameters of the inspection system by using specific imaging parameters (resolution, lighting conditions, angle of view) and image processing parameters (threshold values, detection algorithms) to enhance the detection of subtle bonding defects. These parameter optimizations allow the system to achieve high detection accuracy without significantly increasing device complexity.
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
Enables rapid detection of bonding defects with high accuracy by deforming and visually distinguishing defective areas from non-defective areas using ultrasonic standing waves.
Implementation Method 1
an ultrasonic wave generator that generates ultrasonic waves
Implementation Method 2
a standing wave generator, generating a standing wave
Implementation Method 3
applying the standing wave that is generated to the inspection target to apply a suction force to the bonding article
Data Source
AI summary
A defect detection device (100) which detects a defect of a semiconductor device (10) includes: a standing wave generator (20), applying a standing wave (30) to the semiconductor device (10) to apply a suction force to a wire (13); cameras (41, 42); and a control part (50), adjusting an operation of a standing wave generator (20) and performing defect detection on the semiconductor device (10). The control part (50) captures, by using the cameras (41, 42), a first image of the semiconductor device (1) of a first state in which the suction force is applied to the wire (13) and a second image of the semiconductor device (10) of a second state in which the suction force applied to the wire (13) is smaller than the first stage, and compares the first image and the second image to perform defect detection.