Features Articles

Kent Balius

A top-down approach for reducing error-prone and time-intensive manual operations.

Last month we talked about the often ambiguous, unstructured design data packages running rampant in the PCB industry, which drive non-value-add administrative tasks across all phases of our data exchange and processes, and we underscored the urgency to integrate “smart engineering” data-driven processes, as becoming more efficient as an industry in reducing cost and NPI cycles should be a critical objective to all organizations. What exactly do we mean when we talk about smart engineering or data-driven processes? Buzzwords and acronyms are all around us, such as digital transformation, RPA (repetitive or robotic process automation), BPM (business process management), SaaS (software as a service), etc. All encompass a similar objective: optimizing our processes throughout the enterprise.

In the PCB manufacturing facility, some classic examples of duplicated data entry when receiving a new design package are in the front-end engineering process steps (FIGURE 1). Several generic steps occur across the industry, and all of these must occur, with the sequence varying based on the company or manufacturing facility. In many cases, each of these process steps are completely segregated software applications, which in essence results in non-value-added administrative tasks.

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Bob Willis

AOI during fabrication will catch most pad shorts caused by etching.

This month we look at etching faults on PCBs. This is no ordinary set of pads. They are for an 01005 chip capacitor, the second-smallest chip component available. (Yes, there is an even-smaller size.) The 01005 component package is approximately 0.016" by 0.008" or 0.4mm by 0.2mm and small enough for most members of your staff.

Unfortunately, we found small copper shorts between the two mounting pads on one of our test boards that were not picked up during fabrication. The defective boards were spotted during printing trials. The nickel and gold plating are also present. As with many copper shorts, this is related to bare board etching and imaging, but they should have been picked up earlier.



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Robert Boguski

An out-of-body experience leads to introspection.

By the time you read this, I’ll be having an anniversary of sorts. Commemorating, not celebrating.

Your life can change in an instant. Let me explain.

Two years ago, at the end of May 2019, our team exhibited a new CT scanning machine at an aerospace trade show in Southern California. Nothing newsworthy there. Display the machine, a kind of entry-level CT scanning system; answer questions from any and all; harmlessly scan a few souvenir water bottles to interactively show the novelty of nondestructive 3-D imaging. Do the usual glad-handing and manufactured sincerity that comes with the show gig. Expectantly snag a few promising leads over three tedious days. Inspire somebody to part with their cash. Show team solidarity around our maypole of a machine by memorializing the moment with a group photo. Say kumbaya, crate it up and dodge forklifts while prepping for shipment back north to our facility. There the system will go into working display as a demo unit. Goodbye, crate. Mission accomplished, take the rest of the week off and enjoy the sights and sounds of Southern California, rekindling my youth and visiting friends, savoring the week’s success over cocktails with broiled fish in Seal Beach. Life is good.

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Hom-Ming Chang

How an app approach to data analysis cut initial data formatting time and sped defect resolution.

A key tenet of Lean manufacturing is to reduce variation through process standardization and control. To that end, most companies develop a control plan and monitor various steps of the process. The data collected in those monitoring activities are also useful in facilitating continuous improvement activities. This is particularly true as automated data collection technology has evolved and made it easier to share across multiple platforms.

For example, SigmaTron’s team in its Suzhou facility uses a combination of enhanced inspection equipment, a proprietary manufacturing execution system (MES) and a newly created IT tool to drive continuous improvement efforts.

These efforts build on a Lean manufacturing approach that includes design for manufacturability (DfM) recommendations made to eliminate defect opportunities prior to the new product introduction (NPI) process and use of a production part approval process (PPAP) methodology during the NPI process.

Since the facility’s focus is predominately higher volume production, its SMT lines are optimized to include a higher level of in-process inspection, utilizing 3-D solder paste inspection (SPI) following paste or glue deposition and automated optical inspection (AOI) both pre- and post-reflow. The MES collects yield data at those points and during in-circuit and functional test. The MES also tracks assemblies through each production step in the routing to support traceability requirements.

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Alun Morgan

How AI and lab-on-a-chip pave the way for economical solutions.

We could be moving into the end game with the Covid-19 pandemic, at least as far as the severest effects are concerned. Clearly, the virus and its mutations are here to stay, and the future will be about protecting us through immunization and developing better treatments. The fact that effective vaccinations have become available only a year after the pandemic was recognized is remarkable. It’s partly due to the speed with which researchers have been able to do the data crunching needed to model and understand how best to attack the virus.

In the past, the computations involved in sequencing the virus DNA would have taken vast quantities of computer time and prolonged development of the vaccine. Cloud computing using AI accelerators has dramatically shortened the time to complete the technical work involved in creating the vaccines now being rolled out.

It would be great if we could harness our technologies to create an early warning system when clusters of unusual diseases or events occur anywhere in the world. That’s exactly what organizations like BlueDot are doing right now. Indeed, BlueDot says it spotted the cluster of unusual pneumonia cases in Wuhan in December 2019 that we now know was the coronavirus. To monitor the spread of infectious diseases around the world, it analyzes a huge number of variables, not only official public health data but also climate information, international travel patterns, animal and insect population data, and others. This relies on the ability of AI to detect patterns, and exceptions to those patterns, hidden within the enormous body of information. By sifting through the reports and data points collected every few minutes, 24 hours a day, from sources around the world, using techniques like machine learning and natural language processing, BlueDot brings a small number of cases to the attention of experts for further investigation. Only with AI do we have a hope of finding those cases.

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Greg Papandrew

“We have used this vendor for years” is not a viable strategy.

As the price of PCB materials continues to skyrocket, why are some circuit board buyers stuck firmly in the past, doing business as they always have? Why, even when paying more than they should, do they fear upsetting the apple cart?

Bare board buying can be competitive, but only if those overseeing their company’s PCB supply chain are willing to occasionally buck a system put in place years ago. For circuit board buyers and procurement managers in particular, I see three ingrained habits that do damage to a firm’s PCB purchasing program and its ability to get competitive pricing.

1. Buyers are untrained. One outdated practice in the PCB industry that always amazes me is the willingness to throw buyers into the deep end without giving them 21st century training on how to buy boards. Does management assume PCB buyers will gain all the knowledge they need on the job? Sometimes, they probably do. But often, they end up costing their companies a lot of money as they learn from their mistakes.

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