Publication | Closed Access
A reference-free micro defect visualization using pulse laser scanning thermography and image processing
19
Citations
15
References
2016
Year
EngineeringMicroscopyMicro DefectMicroscopy MethodOptical DiagnosticsInstrumentationEdge DetectionImage ProcessingMicroanalysisThermal ImagingQuality ControlAutomated InspectionMicrostructureThermographyMicroscope Image ProcessingMicrofabricationInfrared SensorScientific VisualizationScanning Probe MicroscopyEdge Detection AlgorithmThermal SensorInfrared ImagingPulse Laser
As quality control of micro devices and early detection of micro defects in these devices are becoming increasingly important, the demand for a fast and automated online inspection technique to detect micro defects with high spatial resolution is increasing. In this study, a reference-free micro defect visualization algorithm is developed based on laser scanning thermography to detect micro defects in devices instantaneously and automatically with high spatial resolution. A pulse modulated continuous wave laser generates thermal waves in a target device, and the corresponding thermal responses are recorded by an infrared (IR) camera. When the thermal wave encounters a micro defect, the propagation of the thermal wave is blocked at the interface of the micro defect. The blockage of the thermal wave is detected by the proposed reference-free micro defect visualization algorithm. First, an edge detection algorithm is applied to a raw thermal image obtained at a specific time point to extract the thermal discontinuities formed at the boundaries of the defect. The edge images obtained from all time sequences are then assembled into a single accumulated edge image to accentuate defect-induced thermal disturbances in the form of edge features. Finally, the accumulated edge image is automatically processed using a binary imaging algorithm to visualize the micro defect in the target device. The performance of the proposed reference-free micro defect visualization algorithm is examined using two types of specimens, semiconductor chips and ceramic-epoxy composites. The proposed algorithm successfully diagnoses micro defects ranging from 4 μm to 40 μm in width.
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