Why

In the electronic industry there are compelling reasons to adopt X-Ray-based machine vision systems. The cost of failure becomes significant when faulty product is shipped to a consumer. Detecting rejects immediately after a value-adding step such as soldering can significantly reduce warranty costs, field repairs, etc.

Furthermore, the shrinking sizes of passive components (resistors as small as 0.02' x 0.01') and finer line widths of printed circuit boards all reflect the trend to more dense board designs as designers attempt to get more functionality on less real estate. Consequently, solder joints are ever more critical as they in turn are smaller and closer together, making them more difficult to inspect by people.

Another reason for the adoption of X-Ray-based machine vision systems is that even if people can inspect solder joints, studies have demonstrated repeatedly that people are very subjective and the results of inspection cannot be duplicated from inspector to inspector or even by the same inspector. The result of using human inspectors to inspect solder joints, especially where the population density of a board is high, is inevitably a high incidence of poor solder joints escaping detection while at the same time a high incidence of solder joints being falsely perceived as rejects. This brings up the third reason for using X-Ray-based machine vision systems to inspect solder joints, That is, a high incidence of false rejects leads to excessive rework and inevitably that rework results in damage to the board creating scrap.

Another rationale in the case of high density boards where test probe points are difficult to establish is that X-Ray-based machine vision systems can eliminate the need for in-circuit testing or at the very least increase yields at in-circuit and functional testing.

Studies have also suggested that 25% of all post-solder defects cannot be detected electrically. These include fault types such as insufficient solder, cold solder joints, misaligned components, voids and excess solder. All of these can be detected by X-Ray-based systems.

Applications in Electronics

In the electronics industry, X-Ray-based machine vision systems are used to inspect the inside of integrated circuit packages to assure package and interconnect integrity (perhaps an application more identified with the semiconductor industry), to align board layers in multilayer manufacturing, to inspect multilayered boards before hole drilling to qualify hole-pad alignment and to inspect solder joints. By far the largest market is the last application. There are a number of reasons why the solder joint inspection application is being more widely addressed by X-Ray-based machine vision systems. With the increased adoption of chip scale technology, such as ball grid arrays, and the result that solder joint interconnects are hidden from view there is no way that people really perform a comprehensive inspection. Similarly, conventional machine vision systems cannot see the solder joints.

Technology Readiness

Significantly, as the soldered assembled board application becomes more demanding, the technology that serves as the basis for responding has also emerged. Microfocus X-Ray systems are now available compatible with the requirements to detect artifacts a few microns in size. Solid-state cameras including those made of amorphous silicon are available with the properties required to handle the dynamic ranges of X-Ray images. Microprocessor power has evolved to the point where three-dimensional data can be processed, modeled and analyzed fast enough to keep up with many production lines.

Types of Systems

Not all X-Ray systems used in the electronic industry are machine vision-based. It turns out there are various degrees of "machine vision-ness." Many in the industry still rely on real-time X-Ray systems. These are no more than closed-circuit TV systems where the image is derived from an X-Ray of the scene. In these cases, the operator makes the decision regarding solder joint quality, so there is still substantial subjectivity in the decision process. These systems are often used to perform line set-up analysis -- first piece approval, sample inspection, or as an engineering tool to debug the manufacturing of a new board design.

The next iteration is one that incorporates machine vision-based software to enhance the X-Ray image to reduce noise, increase the contrast separation between acceptable and unacceptable conditions and/or enhance edges. Some of these packages come with additional tools such as "calipers" to aid an operator in making dimensional checks on artifacts such as solder voids. Some come with specific tool sets for specific components; e.g. BGA, flip chips, etc. Some come with sufficient memory to store the images of 30 or more sectors of an assembled board giving them the ability to make comparisons against the stored references to detect changes that could signify a solder concern. Some of these systems are further "automated" in that there stages can be programmed to follow a specific path or sequence to assure all solder joints are inspected systematically by the operator.

The ultimate class of these X-Ray systems are those that incorporate machine vision-based automated defect detection capabilities as well as machine vision-based image processing and enhancement. These do not require any operator intervention to determine if a solder joint is acceptable or not. These systems have typically been used in an offline scenario but some are emerging that are fast enough to be used for many online applications as well.

Fundamental Technical Approaches

There are two fundamental approaches to these fully automated X-Ray-based machine vision systems: transmission and laminography. In the transmission-based system defects can be correlated to the optical density map of the scene, which, in turn, corresponds to the attenuation map stemming from the actual object density variables in the X-Ray image. While these systems can tell whether there is sufficient, insufficient or too much solder at a joint they cannot tell much about the shape of the solder joint itself. Solder purists suggest that the quality of a solder joint is as much a function of the amount of solder as it is the shape of solder fillet. Another potential problem with transmission-based systems is their ability to reliably inspect double-sided boards because board and component properties may hide or alter solder joint images.

The laminography approach essentially takes cross-sectional views along the z-axis of the solder joint so it examines not only the two-dimensional optical density profile but it does this for each slice or cross-sectional view. In other words, a three-dimensional picture is captured of the solder joint and that is the profile that is essentially compared to the "golden" profile for that specific solder joint. Double-sided assemblies can be inspected in one pass.

Which to Buy

The type of system that one buys will be determined by how it will be used. If it were to be an engineering tool, a real-time X-Ray system with image analysis software would probably be sufficient. If it is to be a process improvement tool then a fully automated system including machine vision-based automated defect recognition tools will be required. In the latter case, considerations include type of facility -- low volume/high mix, medium volume/medium mix or high volume/low mix. In the latter case systems may struggle to perform 100% inspection -- that is, inspect 100% of all the solder joints on 100% of the boards produced. In this case it may make sense to combine an automatic optical inspection system to inspect the more conventional components and use the X-Ray-based system to inspect those components whose designs tend to hide the solder joints. Certainly on those lines where high value boards are produced or boards are produced whose failure would lead to security or safety issues (e.g. many medical devices, automotive, military), then a fully automated system should be considered.

Ken Pelzel of Agilent Technologies made the following observation regarding applications: "A better way to look at the problem is board complexity, repair strategies, and quality goals. Low mix, high volume which are simple, single-sided consumer products which are not repaired and there isn't high end user expectations on quality, then X-Ray is not a good fit. Conversely, low mix, high volume, high complexity, double-sided consumer products such as a laptop is a reasonable fit."

Justification

As suggested in our earlier article on machine vision-based automatic optical inspection systems for assembled board applications, the purchase of an automated, machine vision-based X-Ray system can be easily justified. The analysis suggested in that article applies as well to these systems. Factors in justification include predictions of yield improvement, scrap reduction, rework costs, etc. The greater the board component density, higher throughput required, more inaccessible test nodes and higher the logic complexity, the easier these systems are to justify as the first-pass yield will be proportionately greater where such systems are used as process monitors and immediate corrective action is taken as required to avoid the production of defects.

Questions to Ask Prospective Vendors of X-Ray-Based Machine Vision Systems

In choosing an X-Ray-based machine vision system many of the issues associated with the selection of a machine vision-based AOI system apply. As you examine the products from different vendors you will find most make the same claims. It is clearly important to get them to put their claims in writing. The following questions are meant to provide the framework for a systematic analysis of the competitive landscape. The answers given should be consistent with the application requirements anticipated. This list is not meant to be complete. Because of different quality management philosophies within the board assembly industry, the set of questions used should be consistent with your own espoused quality strategy.

For which applications does your company offer products: post reflow, post wave solder? Other?

Regarding your system:
Is your system an on-line or offline system? Note: If both, please answer the following questions for each style.
Does it perform 100% inspection or sample inspection? In either case, please provide some measure of board density versus throughput? E.g. For board with 4000 components on an 8' board, system can handle 2 sq. in/second or whatever.
Is your system 2D or 3D?
What does the system do and what are the specs? In italics are the likely responses to anticipate.

• Component presence -- most likely do


• Component missing -- most likely do


• Correct component -- unlikely


• Polarity -- for some components


• Orientation -- for some components


• Misplaced/offset -- most likely do


• Skewed -- most likely do


• Tombstones -- most likely do


• Solder presence -- yes


• Insufficient solder -- most likely do


• Solder bridges both between leads and between components -- most likely do


• Solder wick -- most likely do


• Cold solder joint -- depends


• Dewetting -- depends


• Solder voids -- yes


• Bent leads -- most likely do


• Lifted leads/chips -- most likely do


• Solder balls -- most likely do

What is the finest pad pitch that can be handled?
If 3D what does the system do and what are the specs? Accuracy, repeatability of measurements, etc.?
Do you have a recommended calibration procedure to demonstrate accuracy of the system and, if so, what is it?
If 3D, how is the height of the solder joint measured -- based on a local reference plane? A global reference plane? How does the system handle board warpage issues?
Can your systems handle lead-thru-hole components?
If 2D is system based on area camera or line scan camera?
If 3D can you describe the fundamental underlying principles for capturing 3D data?
How is the system trained to handle a new board design? CAD compatibility? Gerber file compatibility? Train-by-showing? Other? Combination?
How long does it take to train on a new board?
What is the changeover time where boards have been previously trained?
What is the throughput at what specific pixel size?
What is your false reject rate? Escape rate? How have these been demonstrated?
Is there an action that takes place if there are "x" number of consecutive rejects at the same location? Or "Y" over the entire board?
Is the system design based on your own proprietary hardware or commercially available products such as frame grabbers or vision processors or is it a host-based processing system?
Do you offer an upgrade patch for future generation products?
How many cameras does your system have and why?
Does your system have the ability to adapt field-of-view/resolution as a function of the board design for a specific board design?
Does your system have Internet trouble-shooting compatibility?
Can you comment on your system's suitability for: low mix/high volume operations, medium mix/medium volume and high mix/low volume?
Does the system interface to a rework station? Do you offer a rework station?
What is the price range of your systems?
What options, if any, are offered for your system?

The first part of this article concentrated on reviewing applications of machine vision-based X-Ray systems in the electronic industry. In this part we will cover some of the applications in other industries.

Real-time X-Ray systems have been used for many years as an integral part of a non-destructive evaluation program where the risk of failure has been high. Thanks to lawyers, the cost of these risks has escalated in recent years. Theoretically this should make the argument for adopting machine vision-based X-Ray systems even more compelling. Certainly this is the case for those failure modes associated with products that pose a danger to the user.

As suggested in Part 1 of this article, there are various degrees of ''machine vision-ness.'' Many applications in industry still rely on real-time X-Ray systems. These are no more than closed-circuit TV systems where the image is derived from an X-Ray of the scene. In these cases, the operator makes the decision regarding integrity of the object being X-Rayed, so there is still substantial subjectivity in the decision process.

The next iteration is one that incorporates machine vision-based software to enhance the X-Ray image to reduce noise, increase the contrast separation between acceptable and unacceptable conditions and/or enhance edges. Some of these packages come with additional tools such as ''calipers'' to aid an operator in making dimensional checks on artifacts such as solder voids. Some come with specific tool sets for specific objects/materials being inspected. Some of these systems are further ''automated'' in that their stages can be programmed to follow a specific path or sequence to take multiple views of an object systematically.

The ultimate class of these X-Ray systems are those that incorporate machine vision-based automated defect detection capabilities as well as machine vision-based image processing and enhancement. These do not require any operator intervention to determine if the product or material being X-Rayed is acceptable or not. These systems have typically been used in an offline scenario but some are emerging that are fast enough to be used for many online applications as well.

Applications

Food Industry

Following the electronic industry, the industry that is the next largest user of machine vision-based X-Ray systems is the food industry. It is likely that every hamburger patty and chicken fillet served by a fast food franchise establishment has been X-Rayed to make sure there are no bones or other foreign objects. Systems can handle either cooked or raw products. Typically these systems inspect these products in bulk form, either delivered in slurry through a pipe or on a conveyor. In addition to the meat products cited, these bulk inspection systems are also being applied to inspect nuts, potatoes, cookies/crackers, salty snack foods, tobacco, dried vegetables, cereal, etc. Both metallic and non-metallic foreign objects can be detected: foil wrappers, stainless steel, glass, mineral stones, rubber, high-density plastics, etc. Sensitivity, that is size of foreign object detected, usually depends on speed, specific product and overall area or volume.

Many of the suppliers of these products have also adapted them to inspection of discrete objects, e.g. packages. In these instances they not only inspect for metallic and non-metallic foreign objects but they also can detect presence and completeness of contents in the package as well as incorrect counts, misshapen product, excessive settling. These systems can be used by fresh and frozen food, beverage, dairy, baked goods, snack foods, confectionary, pharmaceutical, health foods, liquids, soaps and sauces, loose powders and granular materials and seafood producing companies. These systems can inspect product in a variety of packages -- glass, metal, plastic, paper. Companies also offer versions adapted for case inspection for presence/absence, etc.

In consumer goods packaging and beverage markets another application is fill height validation. An X-Ray-based system offers advantages over other high-energy detection approaches, such as gamma rays, because the X-Ray source can be turned off when not in use, posing less of a hazard. These systems can detect underfill as well as overfill in steel, aluminum, glass, plastic and paper containers.

Other Industries

In other industries associated more with durable goods manufacturing, X-Ray-based machine vision systems are used wherever safety and reliability are an issue. This includes the inspection of castings used in the aerospace and automotive industries, tires for both automotives and aircraft, assemblies such as air bag assemblies used in the automotive industry. Among the more interesting applications are those in the wood industry where X-Ray-based machine vision systems are being used to grade wood based on density changes as well as to maximize the wood yield by identifying internal defects and their location.

Questions to Ask Prospective Vendors of X-Ray-Based Machine Vision Systems

As you examine the products from different vendors you will find most make the same claims. It is clearly important to get them to put their claims in writing. The following questions are meant to provide the framework for a systematic analysis of the competitive landscape. The answers given should be consistent with the application requirements anticipated. This list is not meant to be complete.

For which specific applications does your company offer products?

Regarding your system:
Is your system an on-line or offline system? Note: If both, please answer the following questions for each style.
What does the system do and what are the specs?
Size of anomalies system can reliably detect?
Do you have a recommended calibration procedure to demonstrate accuracy of the system or the reliability of detection you specify and, if so, what is it?
Is system based on area camera or line scan camera?
Can you describe the fundamental underlying principles for capturing defect image data?
How long does it take to train the system on a new product? Or different model of the same product line?
What is the changeover time where different products have been previously trained?
What is the throughput at what specific pixel size or anomaly size?
What is your false reject rate? Escape rate? How have these been demonstrated?
Is there an action that takes place if there are "x" number of consecutive rejects?
Does the system have the ability to archive detected anomalous conditions? If so how many can it archive?
Is the system design based on your own proprietary hardware or commercially available products such as frame grabbers or vision processors or is it a host-based processing system?
Do you offer an upgrade patch for future generation products?
How many cameras does your system have and why?
What type of camera is used and why? (line scan, area scan, amorphous silicon) Does your system have the ability to adapt field-of-view/resolution as a function the model size being inspected?
Does your system have Internet trouble-shooting compatibility?
What is the price range of your systems?
What options, if any, are offered for your system?

This checklist is meant to be an example and not necessarily comprehensive. For your application there are likely to be other questions that should be asked and whose answers should be the basis of determining the appropriate vendor. The key is to use a systematic approach when evaluating vendors, one that minimizes any bias that might creep in because of the "smoothness" of the salesperson or a vendor's halo effect.

Reprinted with permission from www.machinevisiononline.org, the Automated Imaging Association website.