Defect detection techniques robust to process variation in semiconductor inspection

Abstract

The downscaling of device dimensions in semiconductor manufacturing has meant that critical defect sizes have become smaller and smaller. This makes it more likely that the highly sensitive optical wafer inspection tool used for detecting small defects will erroneously detect process variations as defects, and generate a large amount of ‘nuisance’ information. Therefore, the scanning electron microscope (SEM)-based review tool used needs to automatically discriminate between defects and nuisance information. To identify nuisance information, the absence of defects in the SEM image needs to be accurately detected through an inspection process using the review tool. We propose a defect detection method using (a) the integration of multiple comparison detection (IMCD) results to minimize the number of defect candidates and (b) discrimination based on a normal patch image model (DNPM) to judge whether a candidate is a defect or normal. An evaluation using SEM images of a processed wafer revealed that combining the IMCD and DNPM methods achieves a nuisance information discrimination rate of 84.4% and a defect detection rate of 93.3%, which are higher rates than those achieved by the one-class support vector machine. The proposed methods automatically collect defect images efficiently even when a great deal of nuisance information is produced by the optical wafer inspection tool, and enable manual visual checks to be reduced.

Optical wafer defect inspection at the 10 nm technology node and beyond

“…Conventional optical wafer defect scan is the standard methodology in the industry because of its high throughput and sensitivity [123]. However, the defects reported by a standard optical defect inspection tool require a separate SEM review to determine if the defects are patterning defects [124]. Optical scatterometry [125,126], which is also referred to as optical critical dimension metrology, measures profile parameters of periodic nanostructures by leveraging the diffracted polarization properties of light on the wafer [127][128][129][130], i.e.…”

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.…”

“…Table 10 summarizes the other image acquisition techniques and auxiliary systems used for AOI in literature. Table 4 SEM [111], [113], [117], [126], [127], [134], [135], [318] [44] [315] OCT [230], [278], [313], [314] Thermography [209], [319] X-ray [197], [204], [205], [208], [319]- [321]…”

Electron microscopy in semiconductor inspection

“…In order to automatically discriminate between defects and nuisance information, Harada et al [56] have proposed a true defect detection method through 2 steps. In the first step, multiple reference images are used to decrease the number of defect candidates since true defects are detected even compared to any reference image.…”