US5751910A — Neural network solder paste inspection system

Claim 1

Independent Claim 190 words

A training a neural network, comprising the steps of: providing a first training data base of crisp input and crisp output pairs; fuzzifying crisp outputs of said first training data base to create a second training data base of crisp input and fuzzified output pairs; and training the neural network with said crisp input and fuzzified output pairs to solution conversions, wherein: said crisp outputs are fuzzified in accordance with a predetermined nearness relationship between different crisp outputs, the neural network is employed for classification of objects, the crisp output of each crisp input and crisp output pair is representative of an image of a particular object, and the crisp output of each such crisp input and crisp output pair represents a desired classification of the particular object, and the objects comprise solder paste bricks on at least one printed circuit board, the crisp input of each crisp input and crisp output pair represents a machine vision image of a particular solder paste brick, and the crisp output of each such crisp input and crisp output pair represents an operator generated physical quality score for the particular solder paste brick.

Claim 2

Dependent Claim 190 words

A training a neural network, comprising the steps of: providing a first training data base of crisp input and crisp output pairs; fuzzifying crisp outputs of said first training data base to create a second training data base of crisp input and fuzzified output pairs; and training the neural network with said crisp input and fuzzified output pairs to solution conversions, wherein: said crisp outputs are fuzzified in accordance with a predetermined nearness relationship between different crisp outputs, the neural network is employed for classification of objects, the crisp output of each crisp input and crisp output pair is representative of an image of a particular object, and the crisp output of each such crisp input and crisp output pair represents a desired classification of the particular object, and the objects comprise solder paste bricks on at least one printed circuit board, the crisp input of each crisp input and crisp output pair represents a machine vision image of a particular solder paste brick, and the crisp output of each such crisp input and crisp output pair represents an operator generated physical quality score for the particular solder paste brick.

Claim 3

Dependent Claim 38 words

The method of claim 1 further comprising the step of extracting values for a predetermined set of metrics from the machine vision image of a particular solder paste brick and using said extracted values as the crisp input.

Claim 4

Dependent Claim 110 words

A method of determining a quality score of a test solder paste brick with a neural network trained in accordance with the method of claim 2, comprising the steps of: acquiring a machine vision image of the test solder paste brick; extracting values for the predetermined set of metrics for the test solder paste brick from the acquired machine vision image of the test solder paste brick; applying the extracted values for the test solder paste brick as an input to the trained neural network to produce a fuzzy network output vector; and defuzzifying the fuzzy network output vector to provide a quality score for the test solder paste brick.

Claim 5

Independent Claim 143 words

A method for training a neural network to automatically provide a quality score of the physical quality of a test solder paste brick, comprising the steps of: acquiring an image of each brick of a training set of solder paste bricks; extracting values for a predetermined set of metrics from the acquired image for each brick of the training set; defining a desired quality score for each brick of the training set; fuzzifying the desired quality score for each brick of the training set into a fuzzified quality score vector for each brick in accordance with a predetermined nearness relationship between different quality scores; and applying the extracted values as network inputs and the fuzzified quality score vector as a fuzzy network output vector for each brick of the training set to the neural network in training the neural network to solution convergence.

Claim 6

Independent Claim 143 words

A method for training a neural network to automatically provide a quality score of the physical quality of a test solder paste brick, comprising the steps of: acquiring an image of each brick of a training set of solder paste bricks; extracting values for a predetermined set of metrics from the acquired image for each brick of the training set; defining a desired quality score for each brick of the training set; fuzzifying the desired quality score for each brick of the training set into a fuzzified quality score vector for each brick in accordance with a predetermined nearness relationship between different quality scores; and applying the extracted values as network inputs and the fuzzified quality score vector as a fuzzy network output vector for each brick of the training set to the neural network in training the neural network to solution convergence.

Claim 7

Dependent Claim 105 words

A method of determining a quality score of a test solder paste brick with a neural network trained in accordance with the method of claim 4, comprising the steps of: acquiring an image of the test solder paste brick; extracting values for the predetermined set of metrics for the test solder paste brick from the acquired image of the test solder paste brick; applying the extracted values for the test solder paste brick as inputs to the trained neural network to produce a fuzzy network output vector; and defuzzifying the fuzzy network output vector to provide a quality score for the test solder paste brick.

Claim 8

Independent Claim 87 words

A method for automatically determining a quality score for a test solder paste brick, comprising the steps of: acquiring an image of the test solder paste brick; extracting values for a predetermined set of metrics from the acquired image; applying the extracted values as inputs to a neural network trained using a fuzzified network output vector to produce a fuzzy network output vector for the test solder paste brick; and defuzzifying the fuzzy network output vector to provide a quality score for the test solder paste brick.

Claim 9

Independent Claim 87 words

A method for automatically determining a quality score for a test solder paste brick, comprising the steps of: acquiring an image of the test solder paste brick; extracting values for a predetermined set of metrics from the acquired image; applying the extracted values as inputs to a neural network trained using a fuzzified network output vector to produce a fuzzy network output vector for the test solder paste brick; and defuzzifying the fuzzy network output vector to provide a quality score for the test solder paste brick.

Claim 10

Dependent Claim 26 words

The method of claim 6 wherein the image of the test solder paste brick is acquired using off axis illumination, dark field reflection and cross polarization.

Claim 11

Dependent Claim 35 words

The method of claim 7 applied to multiple test solder paste bricks, and further comprising the step of graphically displaying quality scores for the multiple test solder paste bricks to provide a quality trend indication.

Claim 12

Dependent Claim 51 words

The method of claim 8 applied to real time inspection of the physical quality of solder paste bricks for fine-pitch surface mount components in a printed circuit board assembly line, and further comprising the step of employing the quality trend indication as real time feedback for solder paste brick quality control.

Claim 13

Dependent Claim 29 words

The method of claim 6 wherein the predetermined set of metrics includes brick width, brick length, brick area, longitudinal symmetry, mean intensity, brick edge raggedness and brick corner roundness.

Claim 14

Dependent Claim 97 words

Apparatus for inspecting fine-pitch surface mount solder paste bricks on printed circuit boards and automatically providing a quality score of the physical quality of the bricks, comprising: a camera for acquiring an image of a solder paste brick being inspected; a computer for extracting values of a predetermined set of brick metrics from the acquired image; and a neural network hosted by the computer and trained with a fuzzy network output vector, said neural network having the extracted values applied as inputs to the neural network and outputting a fuzzy quality score for the brick being inspected.

Claim 15

Dependent Claim 97 words

Apparatus for inspecting fine-pitch surface mount solder paste bricks on printed circuit boards and automatically providing a quality score of the physical quality of the bricks, comprising: a camera for acquiring an image of a solder paste brick being inspected; a computer for extracting values of a predetermined set of brick metrics from the acquired image; and a neural network hosted by the computer and trained with a fuzzy network output vector, said neural network having the extracted values applied as inputs to the neural network and outputting a fuzzy quality score for the brick being inspected.

Claim 16

Dependent Claim 32 words

The apparatus of claim 11 further comprising a defuzzifier that defuzzifies the fuzzy quality score outputted by the trained neural network to provide a crisp quality score for the brick being inspected.

Claim 17

Dependent Claim 29 words

The apparatus of claim 12 further comprising an off-axis source of polarized light for illuminating the brick being inspected, and a cross polarizer at an input of said camera.

Claim 18

Dependent Claim 36 words

The apparatus of claim 13 further comprising means for controllably positioning the camera relative to a printed circuit board such that the camera acquires an image of a particular solder paste brick desired to be inspected.

Claim 19

Dependent Claim 34 words

The apparatus of claim 14 wherein the camera comprises a CCD camera, the source of light comprises a halogen light source, and the means for controllably positioning comprises an X-Y linear motor translation stage.

Annuity Stage	Window Opens	Payment Deadline	Surcharge Period Until	Estimated Fee	Status
3.5 Years (Year 4)	2001-05-11	2001-11-10	2002-05-12	$2000.00	Window Closed / Lapsed
7.5 Years (Year 8)	2005-05-11	2005-11-10	2006-05-12	$3760.00	Window Closed / Lapsed
11.5 Years (Year 12)	2009-05-11	2009-11-10	2010-05-12	$7700.00	Window Closed / Lapsed

Neural network solder paste inspection system

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