A line extraction method for automated SEM inspection of VLSI resist

Shu, D.B.; Li, C.C.; Mancuso, J.F.; Sun, Yue

doi:10.1109/34.3875

Cited by 22 publications

(9 citation statements)

References 10 publications

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“…(20,26) The program failed to recognize a 90 degree rotation and 50% scaling (see Table VI and 0.0 14.0 (9, 10) to (9,24) 85.0 9.0 (9, 24) to (18,24) 85.0 7.0 (10,18) to (17,18) for the normal and 5 degrees for the angle (see Table VIIl). (15,12) to (15,18) 70.0 27.5 (4,14) to (30,5) 70.0 24.4 (0, 15) to (23,7) 105.0 9.5 (21,22) to (30,25) 110.0 30.7…”

Section: Simulation Resultsmentioning

confidence: 99%

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Invariant pattern recognition algorithm using the Hough Transform

Li¹

2000

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A new algorithm is proposed which uses the Hough Transform to recognize two dimensional objects independent of their orientations, sizes and locations. The binary image of an object is represented by a set of straight lines. Features of the straight lines, namely the lengths and the angles of their normals, their lengths and the end point 2 positions are extracted using the Hough Transform. A data structure for the extracted lines is constructed so that it is efficient to match the features of the lines of one object to those of another object, and determine if one object is a rotated and/or scaled version of the other. Finally a generalized Hough Transform is used to match the end points of the two sets of lines. The simulation experiments show good results for objects with significant linear features .

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Section: Simulation Resultsmentioning

confidence: 99%

“…Its applications include vehicle guidance [3], gauge inspection [4], detection of seismic patterns [5], recognition of hand printed Hebrew characters [6], automated SEM inspection of VLSI resist [7], etc. Many fast and efficient parallel algorithms and architeetures have been developed to detect straight lines using the Hough Transform.…”

Section: Introductionmentioning

confidence: 99%

Invariant pattern recognition algorithm using the Hough Transform

Li¹

2000

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“…Nearby points in the same row frequently vote to the same cell, sometimes culminating into a false impression of a high peak for a short line [10]. Keeping the row number avoids this problem.…”

Section: Algorithm ( Connective Hough Transform )mentioning

confidence: 99%

“…We are only aware of one effort in solving this problem. Motivated by practical application in integrated circuits, Shu, Li, Mancuso and Sun [10] replaced the counters by bit maps. Instead of accumulating, the appropriate bits in the bit map are set to '1'.…”

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confidence: 99%

Connective hough transform

Yuen

Lam

Leung

1993

Image and Vision Computing

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A method to extend the Hough transform (HT) to detect connectivity by ordered accumulation is reported. The method is applied to the dynamic combinatorial HT [6]. A focus of attention mechanism is also reported. Our connective HT with focus of attention reduces the computational complexity of the DCHT and increases the S/N ratio of the peak in its accumulator. It may be regarded as a principled method for curve tracing. A general method to improve the computational efficiency of the DCHT by probabilistic selection of interesting fixation points is also introduced. Results using simulated and real data are reported. The connectivity problemA common problem to all Hough transform (HT) [4] is the 'connectivity problem'. It arises because the accumulator counts only give the number of points that share the same parameters. These points may not be connected with each other. Hence the number of points is not necessarily a good measure for the strength of the pattern. In the case of straight line detection, Duda and Hart [3] remarked as early as 1972 that "... the (Hough) technique finds collinear points without regard to contiguity. Thus the position of a best-fit line can be distorted by the presence of unrelated figure points in another part of the picture. A related problem is that of meaningless groups of collinear points being detected".Though this is a fundamental problem, it has not received much attention in the past. We are only aware of one effort in solving this problem. Motivated by practical application in integrated circuits, Shu, Li, Mancuso and Sun [10] replaced the counters by bit maps. Instead of accumulating, the appropriate bits in the bit map are set to '1'. After the accumulation is complete, each bit map is searched for sequences of '1' s. Unfortunately, this method amounts to no more than a brute force search. Also, for an TV x N input image, the memory required is increased by }o N N , making this practical only for small images.In the rest of this paper, we shall report a new method, known as Connective Hough Transform (CHT), to add connectivity by ordered accumulation 'supported by a Croucher Foundation fellowship. I thank Jim Stone and David Young for meticulous proofreading, Alistair Bray for supplying the real image, and the referees for thoughtful comments.

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“…Such an approach has two main drawbacks: (a) since each cell in the accumulator array has to include the list of edge segments, the memory space for representation of the accumulator array becomes very large, and (b) it involves least squares ®tting which increase the execution times. Shu et al [15] also used one list for each accumulator cell to store the position of a voting edge point x with respect to a reference point (i.e. the foot of the normal).…”

Section: Introductionmentioning

confidence: 99%

A Real-Time Hough-Based Method for Segment Detection in Complex Multisensor Images

Foresti

2000

Real-Time Imaging

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A line extraction method for automated SEM inspection of VLSI resist

Cited by 22 publications

References 10 publications

Invariant pattern recognition algorithm using the Hough Transform

Invariant pattern recognition algorithm using the Hough Transform

Connective hough transform

A Real-Time Hough-Based Method for Segment Detection in Complex Multisensor Images

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