Method and apparatus for locating, inspecting, and placing large leaded devices

• US 5,805,722 A
• Filed: 10/02/1996
• Issued: 09/08/1998
• Est. Priority Date: 11/22/1993
• Status: Expired due to Term

• Current Assignee: Cognex Corporation
• Original Assignee: Cognex Corporation
• Inventors: Engel, Antonie J., Cullen, Christopher P.
• Primary Examiner(s): Boudreau, Leo
• Assistant Examiner(s): Tadayon, Bijan
• Application Number:
  US08/726,402
• Time in Patent Office: 706 Days
• Field of Search: 364/570, 364/559, 364/490, 348/126, 348/87, 356/380, 356/383, 356/394, 382/141, 382/146, 382/151, 382/199, 382/144, 382/145, 382/147, 382/148, 382/149, 382/150, 382/168, 382/173, 382/181, 382/190, 382/209, 382/224, 382/143, 382/152, 382/169, 382/170, 382/171, 382/172, 382/192, 382/203, 382/204, 382/205, 382/217
• US Class Current: 382/146
• CPC Class Codes: G06T 2207/30141 Printed circuit board [PCB]
• CPC Class Codes: G06T 2207/30148 Semiconductor; IC; Wafer
• CPC Class Codes: G06T 7/73 using feature-based methods

Abstract

In a machine vision system capable of capturing an optical image of a semiconductor electronic component part and digitizing the optical image, a method for locating, inspecting and placing parts known as large leaded devices, by estimating a part'"'"'s location; estimating the approximate location of a group of leads; computing the center and angle of a lead scan search rectangle within which the leads in a group of leads must be located; scanning a lead scan search rectangle to locate lead edges and compile a list of edges; extracting lead positions; updating the part location estimate; and repeating these steps for each lead set to find all leads and lead centerlines; then calculating an optimal part position estimate by matching found lead positions with expected lead centerlines; and finally, inspecting the leads. A statistical lead rejection procedure is included during lead extraction.

Claims

1. In a machine vision system capable of capturing an optical image of a leaded part and digitizing said optical image as a two-dimensional image, a method for locating, inspecting, and placing leaded devices, comprising:

a) estimating an approximate location of a set of leads by locating at least one edge for a lead of a lead set by applying a region of interest window to said image containing a group of leads;

projecting a two-dimensional image into a one-dimensional image;

plotting edge values;

applying an edge filter to locate each edge that may constitute a tip or a base;

scoring the results of the filtering;

selecting from the scoring of the filtering the positions that meet the expected values for a tip edge or a base edge;

b) computing the center and angle of a lead scan search rectangle within which leads in said set of leads must be located by computing two points at either end of a lead set and calculating the average of the points and the angle of a line connecting them;

c) scanning said lead scan search rectangle to locate lead edges and compile a list of edges by covering the search rectangle with a series of stepped calipers, each of said calipers aligned in a non-rotated manner with said image'"'"'s axis and applied in such a way as to guarantee that each lead edge will appear in at least one caliper window and then projecting edge positions along the axis of said lead scan search rectangle, locating projected positions of two edges from different stepped calipers within a specified threshold of each other and selecting the edge with the stronger contrast to compile a single edge list;

d) extracting lead positions;

e) updating a part location estimate; and

f) repeating steps a) through e) for each lead set to find all leads and lead centerlines.

2. The method of claim 1 wherein extracting lead positions further comprises:

a) sorting a list of edges, wherein each edge is described by its position from the start of the first scan, its contrast level, and its contrast polarity, wherein positive contrast polarity goes from dark to light and negative contrast polarity goes from light to dark;

b) finding a group of consecutive edges that might contain two lead edges;

c) find all edge pairings within a group that might constitute a lead and generating a set of lead candidates;

d) adjusting the expected lead width and lead pitch, in terms of the distance to the previous lead, to compensate for any difference between scan angle and lead set angle;

e) picking the lead candidate whose width and pitch best matches the expected width and pitch of a model lead;

f) projecting said candidate'"'"'s lead location onto the midline of said lead scan search rectangle; and

g) converting the lead location into physical coordinates;

repeating steps a) through g) until the entire edge list is processed.

3. The method of claim 2 wherein the lead extraction step further comprises using control parameters to minimize errors.

4. The method of claim 3, wherein the step of using control parameters further comprises:

assigning polarities to starting edges and ending edge descriptions to indicate front lighting or back lighting;

assigning a maximum lead width parameter;

assigning a minimum lead width parameter;

calculating a grade for each lead candidate, wherein the grade is the sum of the deviations from expected width and pitch.

5. The method of claim 4, wherein the method of calculating a grade for pitch further comprises:

a) computing the distance from a lead to a preceding lead or two leads, if they exist;

b) selecting the distance best matching an expected pitch;

c) if the best matching distance is not that from a lead to an immediately preceding lead, eliminate said preceding lead.

6. The method of claim 1 wherein inspecting leads further comprises:

a) compiling statistics on designated properties of a lead set as a whole, heavily weighting the characteristics of leads found in the middle;

b) assigning each lead in a lead set a grade indicating its typically compared to said set as a whole;

c) computing a histogram of the grades of a set;

d) analyzing the histogram;

e) calculating the number of extraneous leads (n)found in a set;

f) selecting from said histogram values, those extraneous leads n, with the worst scores;

g) verifying that the n worst grades are significantly worse than the grades of the rest of the leads;

h) verifying that these n worst graded leads are also at the ends of a lead set; and

i) rejecting said n worst leads.

7. The method of claim 6 wherein compiling statistics further comprises:

a) subtracting twice the number of extraneous leads from the total number of expected leads, yielding a number of remaining leads in the middle of said lead set;

b) calculating lead contrasts from the said remaining leads as the average contrast of two edges comprising the lead;

c) calculating lead width for said remaining leads;

d) computing the average pitch of at least two and at most an upper threshold m of the middle leads found;

e) calculating an ideal base position for a first lead by calculating the average position of the central m leads;

correcting this by subtracting the average pitch times half of one less than the number of leads;

f) computing an expected position for each lead by using the computed average pitch and the ideal base position, so that said ideal base position is equal to lead number--where a first lead is numbered zero--multiplied by average pitch;

g) computing the standard deviation for lead contrast and width for each of the leads being analyzed;

h) calculating the number of standard deviations from average for each of said leads'"'"' width, each of said leads'"'"' contrast and each of said leads'"'"' position'"'"'s deviation from expected position; and

i) calculating a grade for each lead being analyzed by summing each of the standard deviations computed for it in step h that are greater than a specified threshold value.

8. In a machine vision system capable of capturing an optical image of a leaded part and digitizing said optical image as a two-dimensional image, an apparatus for locating, inspecting and placing leaded devices, comprising:

a) a means for estimating an approximate location of a set of leads by locating at least one edge for a lead of a lead set by applying a region of interest window to said image containing a group of leads;

projecting a two-dimensional image into a one dimensional image;

plotting edge values;

applying an edge filter to locate each edge that may constitute a tip or a base;

scoring the results of the filtering;

selecting from the scoring of the filtering the positions that meet the expected values for a tip edge or a base edge;

b) means for computing the center and angle of a lead scan search rectangle within which leads in said set of leads must be located by computing two points at either end of a lead set and calculating the average of the points and the angle of a line connecting them;

c) means for scanning said lead scan search rectangle to locate lead edges and compile a list of edges by covering the search rectangle with a series of stepped calipers, each of said calipers aligned in a non-rotated manner with said image'"'"'s axis and applied in such a way as to guarantee that each lead edge will appear in at least one caliper window, the means for scanning also communicating with a means for projecting edge positions along the axis of said lead scan search rectangle, and a means for locating projected positions of two edges from different stepped calipers within a specified threshold of each other and a means for selecting the edge with the stronger contrast to compile a single edge list;

d) means for extracting lead positions;

e) means for updating a part location estimate; and

f) means for iterating through each lead set to find all leads and lead centerlines.

9. The apparatus of claim 8 wherein the means for extracting lead positions further comprises:

a) a means for sorting a list of edges, wherein each edge is described by its position from the start of a first scan, its contrast level, and its contrast polarity, wherein positive contrast polarity goes from dark to light and negative contrast polarity goes from light to dark;

b) a means for finding a group of consecutive edges that might contain two lead edges;

c) a means for finding all edge pairings within a group that might constitute a lead to generate a set of lead candidates;

d) a means for adjusting the expected lead width and lead pitch, in terms of the distance to the previous lead, to compensate for any difference between scan angle and lead set angle;

e) a means for picking a lead candidate whose width and pitch best matches the expected width and pitch of a model lead;

f) a means for projecting the candidate'"'"'s lead location onto the midline of the search rectangle; and

g) a means for converting the candidate'"'"'s lead location into physical coordinates; and

a means for repeating items a) through g) until the entire edge list is processed.

10. The apparatus of claim 9 wherein the means extracting lead positions further comprises a means for using control parameters to minimize errors.

11. The apparatus of claim 10 wherein the means for calculating a grade for pitch further comprises:

a) a means for computing the distance from a lead to a preceding lead or two leads, if they exist;

b) a means for selecting the distance best matching an expected pitch;

c) if the best matching distance is not that from a lead to an immediately preceding lead, a means to eliminate said preceding lead.

12. The apparatus of claim 8 further comprising:

a) a means for compiling statistics on the properties of a lead group as a whole, heavily weighting the characteristics of leads found in the middle;

b) a means for assigning each lead in a group a grade indicating its typicality compared to said group as a whole;

c) a means for computing a histogram of the grades of a group;

d) a means for analyzing the histogram;

e) a means for calculating the number of extraneous leads (n) found in a group;

f) a means for selecting from said histogram values, those extraneous leads n with the worst scores;

g) a means for verifying that the n worst grades are significantly worse than the grades of the rest of the leads;

h) a means for verifying that these n worst graded leads arc also at the ends of a lead set; and

i) a means for rejecting said n worst leads.

13. In a machine vision system capable of capturing an optical image of a part and digitizing said optical image, to enable the locating, inspecting and placing of leaded devices, a method for statistically rejecting spurious leads comprising:

a) compiling statistics on the properties of a lead group as a whole, heavily weighting the characteristics of leads found in the middle;

b) assigning each lead in a group a grade indicating its typicality compared to said group as a whole;

c) computing a histogram of the grades of a group;

d) analyzing the histogram;

e) calculating the number of extraneous leads (n) found in a group;

f) selecting from said histogram values, those extraneous leads n with the worst scores;

g) verifying that the n worst grades are significantly worse than the grades of the rest of the leads;

h) verifying that these n worst graded leads are also at the ends of a lead set; and

i) rejecting said n worst leads.

14. The method of claim 13 wherein compiling statistics further comprises:

a) subtracting twice the number of extraneous leads from the total number of expected leads;

b) calculating lead contrasts from the remaining leads in the middle as the average contrast of two edges comprising the lead;

c) calculating lead width for said remaining leads;

d) computing the average pitch of at least two and at most an upper threshold m of the middle leads found;

e) calculating an ideal base position for a first lead by calculating the average position of the central m leads;

correcting this by subtracting the average pitch times half of one less than the number of leads;

f) computing an expected position for each lead by using the computed-average pitch and the ideal base position, so that said ideal base position is equal to the lead number--where a first lead is numbered zero--multiplied by the average pitch;

g) computing the standard deviation for lead contrast and width for each of the leads being analyzed;

h) calculating the number of standard deviations from average for each of said lead'"'"'s width, each of said lead'"'"'s contrast and each of said lead'"'"'s position deviation from expected position; and

i) calculating a grade for each lead being analyzed by summing each of the standard deviations.