Completed chips coming in from the foundry are topic to a battery of exams. For these destined for vital methods in vehicles, these exams are significantly intensive and might add 5 to 10 % to the price of a chip. However do you actually need to do each single take a look at?
Engineers at NXP have developed a machine-learning algorithm that learns the patterns of take a look at outcomes and figures out the subset of exams which might be actually wanted and people who they might safely do with out. The NXP engineers described the method on the IEEE Worldwide Take a look at Convention in San Diego final week.
NXP makes all kinds of chips with advanced circuitry and superior chip-making know-how, together with inverters for EV motors, audio chips for client electronics, and key-fob transponders to safe your automotive. These chips are examined with completely different alerts at completely different voltages and at completely different temperatures in a take a look at course of referred to as continue-on-fail. In that course of, chips are examined in teams and are all subjected to the entire battery, even when some components fail a number of the exams alongside the best way.
Chips have been topic to between 41 and 164 exams, and the algorithm was capable of advocate eradicating 42 to 74 % of these exams.
“We’ve to make sure stringent high quality necessities within the area, so we’ve got to do plenty of testing,” says Mehul Shroff, an NXP Fellow who led the analysis. However with a lot of the particular manufacturing and packaging of chips outsourced to different firms, testing is without doubt one of the few knobs most chip firms can flip to manage prices. “What we have been attempting to do right here is provide you with a approach to scale back take a look at value in a method that was statistically rigorous and gave us good outcomes with out compromising area high quality.”
A Take a look at Recommender System
Shroff says the issue has sure similarities to the machine learning-based recommender methods utilized in e-commerce. “We took the idea from the retail world, the place a knowledge analyst can have a look at receipts and see what gadgets persons are shopping for collectively,” he says. “As an alternative of a transaction receipt, we’ve got a novel half identifier and as a substitute of the gadgets {that a} client would buy, we’ve got an inventory of failing exams.”
The NXP algorithm then found which exams fail collectively. In fact, what’s at stake for whether or not a purchaser of bread will wish to purchase butter is kind of completely different from whether or not a take a look at of an automotive half at a selected temperature means different exams don’t must be executed. “We have to have one hundred pc or close to one hundred pc certainty,” Shroff says. “We function in a distinct area with respect to statistical rigor in comparison with the retail world, however it’s borrowing the identical idea.”
As rigorous because the outcomes are, Shroff says that they shouldn’t be relied upon on their very own. It’s a must to “be certain it is smart from engineering perspective and you could perceive it in technical phrases,” he says. “Solely then, take away the take a look at.”
Shroff and his colleagues analyzed information obtained from testing seven microcontrollers and functions processors constructed utilizing superior chipmaking processes. Relying on which chip was concerned, they have been topic to between 41 and 164 exams, and the algorithm was capable of advocate eradicating 42 to 74 % of these exams. Extending the evaluation to information from different varieties of chips led to a good wider vary of alternatives to trim testing.
The algorithm is a pilot undertaking for now, and the NXP group is trying to broaden it to a broader set of components, scale back the computational overhead, and make it simpler to make use of.
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