Classification of Defect Clusters on Semiconductor Wafers Via the Hough Transformation

Abstract

The Hough transformation employing a normal line-to-point parameterization is widely applied in digital image processing for feature detection. In this paper, we demonstrate how this same transformation can be adapted to classify defect signatures on semiconductor wafers as an aid to visual defect metrology. Given a rectilinear grid of die centers on a wafer, we demonstrate an efficient and effective procedure for classifying defect clusters composed of lines at angles of 0 , 45 , 90 , and 135 from the horizontal, as well as adjacent compositions of such lines. Included are defect clusters representing stripes, scratches at arbitrary angles, and center and edge defects. The principle advantage of the procedure over current industrial practice is that it can be fully automated to screen wafers for further engineering analysis.

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

“…For example, at the IC placing stage, defect cases of missing, wrong or doubled components may occur. In terms of possible soldering defects, most of them happen after the reflowing stage, such as the defects at the IC package components (pseudo joint, excess solder, insufficient solder, shifting Solder and bridge defects) and the defects at the non-IC components (side termination, [63] Ring, scratch, zone and repeating types [64] Ring, scratch, random and new patterns [65] Systematic and random patterns [66] Circle, cluster, scratch and spots [67] [68] Bull's Eye, Edge ring, scratch, random, multiple zones, multiple scratches, ring-zone mixed pattern and ring-scratch mixed pattern [69] [70] Multiple zones, multiple scratches, ring-zone mixed pattern and ring-scratch mixed pattern [71] [72] Cluster defects such as scratch, strains and localized failures [58] Checkerboard, ring, right-down edge, composite and random patterns [73] Spatially homogeneous Bernoulli process, cluster, circle, spot, repetitive and mixed pattern [74] Scratch, center and edge [75] Quarter ring, up and left, Quarter ring, up and right, Edge effects, Ring effects, Semi-ring, up, Semi-ring, up Edge effects, up and bottom Cluster [76] Annulus, half-annulus, band and half-ring [77]- [81] Curvilinear, amorphous, and ring [82] Linear and circular patterns [83] [84] Bull's eye, Bottom, Crescent moon, edge and random [85] Random, ring, curvilinear and ellipsoid [59] Line, edge, ring, blob and bull's eye [61] [53] Bull's eye, blob, line, edge, hat and ring [86] Multiple patterns including ring, checkerboard and five radial zones [87] Random, systematic and ,mixed patterns [88] [57] Circle, cluster, repetitive and spot [56], [60], [62], [89]-…”

“…As long as a parameterized model can be established for the spatial pattern, this method can be adopted [59]. White et al in [74] used Hough transform based on linear parameterization to detect defective dies patters on WBM. The patters detected in this study were of linear shapes such as scratches and edge patterns.…”

“…Chang et al in [59] applied Hough transform to detect WM defect patterns and were able to overcome the limitations in previous studies by detecting all the patters that were missed by them. Despite of Hough transform's ability of detecting various defect patterns, this technique is considered insensitive to gaps (missing pixels), which make it difficult to detect random or mixed pattern defects in WMs since it may group them as one pattern instead of multiple ones [74]. Hough transforms performs poorly when it comes to low contrast images, this may not be a problem in WMs since they are generated using circuit probe techniques.…”

Wafer Map Failure Pattern Recognition and Similarity Ranking for Large-Scale Data Sets

“…3) Linear Attribute: The Hough transform [19], which was applied in [23], was used in the current study to detect the lines in each wafer map. The Hough transform algorithm is detailed as follows.…”

Image processing method and implementation for a laser automatic tracking extensometer

“…Hough transformation [6,7] processing, which is widely used in image processing and the domain of computer vision, such as line detection [8,9].…”