Completed chips coming in from the foundry are topic to a battery of exams. For these destined for vital programs in vehicles, these exams are notably in depth and may add 5 to 10 % to the price of a chip. However do you actually need to do each single check?
Engineers at NXP have developed a machine-learning algorithm that learns the patterns of check outcomes and figures out the subset of exams which can be actually wanted and those who they might safely do with out. The NXP engineers described the method on the IEEE Worldwide Check Convention in San Diego final week.
NXP makes all kinds of chips with complicated 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 automobile. These chips are examined with totally different indicators at totally different voltages and at totally different temperatures in a check 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 among the exams alongside the best way.
Chips had 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 to do numerous 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 corporations, testing is among the few knobs most chip corporations can flip to regulate prices. “What we had been making an attempt to do right here is give you a strategy to cut back check price in a means that was statistically rigorous and gave us good outcomes with out compromising area high quality.”
A Check Recommender System
Shroff says the issue has sure similarities to the machine learning-based recommender programs 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 a novel half identifier and as a substitute of the gadgets {that a} client would buy, we’ve 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 need to purchase butter is kind of totally different from whether or not a check of an automotive half at a specific 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 unique house 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. You need to “be certain that it is sensible from engineering perspective and which you could perceive it in technical phrases,” he says. “Solely then, take away the check.”
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 had 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 forms of chips led to a good wider vary of alternatives to trim testing.
The algorithm is a pilot mission for now, and the NXP crew is trying to develop it to a broader set of components, cut back the computational overhead, and make it simpler to make use of.
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