Image analysis – Applications – Manufacturing or product inspection
Reexamination Certificate
1998-08-25
2001-10-02
Boudreau, Leo (Department: 2621)
Image analysis
Applications
Manufacturing or product inspection
Reexamination Certificate
active
06298149
ABSTRACT:
RESERVATION OF COPYRIGHT
The disclosure of this patent document contains material which is subject to copyright protection. The owner thereof has no objection to facsimile reproduction by anyone of the patent document or of the patent disclosure, as it appears in the United States Patent and Trademark Office patent file or records, but otherwise reserves all rights under copyright law.
BACKGROUND OF THE INVENTION
The invention pertains to machine vision and, more particularly, to methods for inspection of leads on semiconductor die packages (or lead frames).
At the heart of an integrated circuit is a semiconductor die. This is a wafer of semiconducting material (e.g., silicon) with hundreds of thousands or millions of electronic circuit components etched into its layers. To enhance processing speed and reduce power consumption, the dies are made as small as possible, e.g., less than a square-inch in area and several mils thick. To facilitate handling, the dies are glued into supporting frames, i.e., lead frames. In addition to providing stability, these frames have large conductive leads that can be soldered to other circuit components, e.g., on a printed circuit board. The leads are typically connected to corresponding pads on the die via a process called wire bonding, wherein a small conductive thread is bonded to each lead and its corresponding pad. Once a semiconductor die and its frame are assembled, they are typically packaged in a ceramic or plastic, forming an integrated circuit.
Inspection of the lead area of the semiconductor die packages is important in the semiconductor industry. Such inspection typically involves checking the leads on the package; both before and after the die is bonded to the package.
The most common defect in assembly is the deposit of unwanted adhesive on the leads. This is sometimes referred to as an AOL defect. Since the adhesive is conductive, it can effectively “short circuit” the semiconductor die's electronic functions.
The inspection of semiconductor packages for adhesive on leads has proven to be a vexing machine vision problem. This is a result of the complexity of the “background,” i.e., the lead pattern which must be inspected in order to find the defect. This is further complicated by the decreasing size, and increasing number, of leads, as well as by the limited resolution of the cameras typically used for inspection. In this regard, it will be appreciated that while there are a variety of lead configurations, there are two basic types: etched leads and flying/free leads. The former are rigid and are etched onto a substrate, while the latter are mechanically pressed but non-rigid.
The prior art suggests the use of a technique referred to golden template comparison (GTC) to inspect the package leads. GTC is a technique for locating objects by comparing a feature under scrutiny (to wit, a lead frame) to a good image—or golden template—that is stored in memory. The technique subtracts the good image from the test image and analyzes the difference to determine if the expected object (e.g., a defect) is present. For example, upon subtracting the image of a good lead frame from a defective one, the resulting “difference” image would reveal an adhesive blotch that could be flagged as a defect.
Before GTC inspections can be performed, the system must be “trained” so that the golden template can be stored in memory. To this end, the GTC training functions are employed to analyze several good samples of a scene to create a “mean” image and “standard deviation” image. The mean image is a statistical average of all the samples analyzed by the training functions. It defines what a typical good scene looks like. The standard deviation image defines those areas on the object where there is little variation from part to part, as well as those areas in which there is great variation from part to part. This latter image permits GTC's runtime inspection functions to use less sensitivity in areas of greater expected variation, and more sensitivity in areas of less expected variation.
At runtime, a system employing GTC captures an image of a scene of interest. Where the position of that scene is different from the training position, the captured image is aligned, or registered, with the mean image. The intensities of the captured image are also normalized with those of the mean image to ensure that variations illumination do not adversely affect the comparison.
The GTC inspection functions then subtract the registered, normalized, captured image from the mean image to produce a difference image that contains all the variations between the two. That difference image is then compared with a “threshold” image derived from the standard deviation image. This determines which pixels of the difference image are to be ignored and which should be analyzed as possible defects. The latter are subjected to morphology, to eliminate or accentuate pixel data patterns and to eliminate noise. An object recognition technique, such as connectivity analysis, can then be employed to classify the apparent defects.
Although GTC inspection tools have proven quite successful, they suffer some limitations. For example, except in unusual circumstances, GTC requires registration—i.e., that the image under inspection be registered with the template image. GTC also uses a standard deviation image for thresholding, which can result in a loss of resolution near edges due to high resulting threshold values. GTC is, additionally, limited to applications where the images are repeatable: it cannot be used where image-to-image variation results form changes in size, shape, orientation and warping.
GTC is typically used to inspect only etched lead configurations, where it can be effectively used if the lead count is not high. Where that count is high, the frequency of etches results in a large area being effectively masked by the high standard deviation at the lead edges. GTC has not proven effective in inspections of flying/free configurations. Moreover, it is limited in that it requires excessive memory or processing time in instances where the package under inspection is rotated.
Blob analysis is also used to inspect etched lead configurations, as well as free-flying lead configurations. However, this analysis technique is only effective if the lead count is not high.
An object of this invention, therefore, is to provide improved methods for machine vision and, more particularly, improved methods for inspecting leads on semiconductor die packages or lead frames.
A further object is to provide such methods that can be used to identify defects such as adhesive blotches on those leads.
Yet another object is to provide such methods that can be used in inspecting the full range of die packages, including both etched lead packages and flying/free lead packages.
Yet still another object is to provide such methods that do not routinely necessitate alignment or registration of an image under inspection with a template image.
Still yet a further object of the invention is to provide such methods that do not require training.
Still other objects of the invention include providing such machine vision methods as can be readily implemented on existing machine vision processing equipment, and which can be implemented for rapid execution without excessive consumption of computational power.
SUMMARY OF THE INVENTION
The foregoing objects are among those achieved by the invention which provides, in one aspect, a machine vision method for inspecting leads on semiconductor die package, or lead frame. The method includes the steps of generating a first image of the lead frame (including, its leads and other structures—together, referred to as the “lead frame” or “background”), generating a second image of the lead frame and any defects thereon (e.g., excessive adhesive), and subtracting the second image from the first image. The method is characterized in that the second image is generated such that subtraction of it from the first image emphasizes the defect with respect to the background.
In re
Nichani Sanjay J.
Scola Joseph
Boudreau Leo
Cognex Corporation
Powsner David J.
Werner Brian P.
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