Development of optical automatic positioning and wafer defect detection system

Abstract

The data of a wafer with defects can provide engineers with very important information and clues to improve the yield rate and quality in manufacturing. This paper presents a microscope automatic positioning and wafer detection system with human-machine interface based on image processing and fuzzy inference algorithms. In the proposed system, a XY table is used to move the position of each die on 6 inch or 8 inch wafers. Then, a high-resolution CCD and one set of two-axis optical linear encoder are used to accurately measure the position on the wafer. Finally, the developed human-machine interface is used to display the current position of an actual wafer in order to complete automatic positioning, and a wafer map database can be created. In the process of defect detection, CCD is used for image processing, and during preprocessing, it is required to filter noise, acquire the defect characteristics, define the defective template, and then take the characteristic points of the defective template as the reference input for fuzzy inference. A high-accuracy optical automatic positioning and wafer defect detection system is thus constructed. This study focused on automatic detection of spots, scratches, and bruises, and attempted to reduce the time to detect defective die and improve the accuracy of determining the defects of semiconductor devices.

Positioning Technology of Tiny Electronic Components Based on Microscope and Computer Vision

“…Therefore, these non-edge points are not suitable for characterizing the sharpness of the image. The conventional method of focusing using image gradients is to simply use the gradient average of the entire image to characterize the sharpness of the image (as shown in Equation 4) [9].…”

A Review and Analysis of Automatic Optical Inspection and Quality Monitoring Methods in Electronics Industry

“…Content may change prior to final publication. [118] Overall quality inspection of wafer [119] IC wafer contamination [120] Micropipes defects [121]- [123] Chip-out, bridging, metal lifting, glassiviation and peel off [124] Wafer topside scratch, foreign material, ink residue, pad damage, passivation/metal damage, ink smeary, and passivation covering [125] Pinhole defects [126] Protrusion, dent, flat and bumpy defects [127] Hole, Protruding and flat patterns [128] Particles, contamination and scratches [129] Defects between line edges [130] Hole, flaw and scratch defects [131] Alignment, probe marks and bump defects for in-tray semiconductor chip [132] Spots, scratches, and bruises [133] Bond pad discoloration [54] Die edge, die street and determination of chipping size and shape [134] Spot, rock-shaped particle, ring-shaped particle, misalignment and scratch [135] Defects are classified as small, medium and large overall functionality of the circuit will be affected. Excess solder joint can cause bridging with other PCB solder joints which can lead to a short circuit.…”

An Artificial-Intelligence-Driven Predictive Model for Surface Defect Detections in Medical MEMS

“…The research used image processing techniques to subtract the reference image (healthy die) from every single die in a wafer and, based on the pixel value of the result image, it was possible to determine whether the dies had defects based on the mean square error value, as long as the defect size was within the detectable limit of the algorithm. Moreover, Tien et al [ 11 ] implemented an automatic positioning and wafer detection system based on image processing and fuzzy inference algorithms. A charge-coupled device (CCD) was used, coupled with pre-processing steps, including noise filtering and edge detection, as well as defining the defective template in order to infer its characteristic points to employ it as the reference input for the fuzzy interface.…”