Results of experiments on PTH parts.

Use of low-temperature solders (LTS) is growing in popularity. LTS are predominantly composed of tin and bismuth, with a small quantity of a “special blend” of other elements to suit a given manufacturer’s performance specifications. The opportunity, as the name suggests, is to create solder joints at far lower temperatures than those required for tin/silver/copper (SAC) alloys, and which are even lower than that needed for the (historic?) tin/lead eutectic solder. These LTS have a melting temperature of ~138°C. The benefits of using LTS mean no Pb is present in the joint, and lower processing temperatures can be used. Using lower temperatures means reduced energy consumption during manufacture, lower manufacturing costs and reduced greenhouse emissions. In addition, it offers the opportunity to use different, thinner and possibly cheaper PCB substrates and components compared with those used today. This obviates the “overengineering” required of today’s boards and components to mitigate warpage, which due to LTS are operating close to the glass transition temperature of the board material. It also makes it possible to rework SAC area array package joints with low-temperature alloys.

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Humans love modifications, but over time they can add up to false comparisons.

“To gild refined gold, to paint the lily ... is wasteful and ridiculous excess.” – William Shakespeare, “The Life and Death of King John”

We are used to editing, cropping and modifying cellphone photos to improve and enhance the original images. This is often achieved by adjusting their contrast and brightness, and applying software filters that could, for example, sharpen or otherwise change the look and details. Why should images taken for x-ray inspection be any different? They are not, of course. The images you see in my columns, as well as in probably every other piece of technical literature, together with virtually every other image seen in today’s media, are likely modified on some level. While we may accept this situation intellectually, I would suggest we often too implicitly trust what our eyes see, and therefore any inherent image manipulation is often taken as fact. This can cause the details we see and accept to possibly mislead us as to the reality of the original. As we use such images to make value judgments on the quality and possible faults in electronics manufacture, is this an issue we should be concerned with, or is it a manipulation we need, but also need to understand why we do it, so we can make the best analysis?

Down the radiation rabbit hole!

Increased use of x-ray inspection throughout the component and PCBA supply chain to check the quality of optically hidden joints and features can elevate a component’s level of radiation dose during assembly. A simple and sensible question, therefore, is can this cause any issues? For example, could this increase the risk of component failure, either a complete, temporary or intermittent failure, that happens during manufacture or potentially at some later time in the field? A sensible and simple question perhaps, but, unfortunately in my experience, one that generates many further questions and uncertain answers.

In general, the industry consensus, together with the reality that x-ray inspection has been widely used for years, is that for most components there is probably not an issue with the radiation doses likely during x-ray inspection. But here we enter our rabbit hole of additional questions: What constitutes the “not most” components?

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What are the questions we should ask before diving in?

To deliberately misquote and mangle Shakespeare once again, I come to praise AI, not to bury it, but does the potential evil it may do live after and the good oft interred in the dataset?

I apologize, but … discussion of the benefits of AI in all manner of applications has been the flavor of the month for much of the last two years, and there seems no end in sight! It has been one of the drivers of processor manufacture and use in recent times. However, two recent articles from BBC News seemed to highlight some pros and cons regarding use of AI for x-ray inspection and test.

The first¹ describes how AI has been trained to best radiologists examining for potential issues in mammograms, based on a dataset of 29,000 images. The second² is more nuanced and suggests that after our recent “AI Summer” of heralded successes on what could be considered low-hanging fruit, we might now be entering an AI Autumn or even an AI Winter. In the future, it suggests, successes with more complex problems may be increasingly difficult to achieve, and attempts are made only due to the hype of the technology rather than the realities of the results.