Completed chips coming in from the foundry are topic to a battery of checks. For these destined for vital techniques in vehicles, these checks are notably intensive and might add 5 to 10 p.c to the price of a chip. However do you really want 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 checks which can be actually wanted and those who they might safely do with out. The NXP engineers described the method on the IEEE International Test Conference in San Diego final week.
NXP makes all kinds of chips with complicated circuitry and advanced chip-making technology, together with inverters for EV motors, audio chips for consumer electronics, and key-fob transponders to safe your automotive. These chips are examined with totally different indicators at totally different voltages and at totally 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 among the checks alongside the way in which.
Chips had been topic to between 41 and 164 checks, and the algorithm was capable of suggest eradicating 42 to 74 p.c of these checks.
“We now have to make sure stringent high quality necessities within the subject, so we’ve to do loads 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 likely one of the few knobs most chip firms can flip to manage prices. “What we had been making an attempt to do right here is provide you with a strategy to cut back take a look at value in a manner that was statistically rigorous and gave us good outcomes with out compromising subject high quality.”
A Take a look at Recommender System
Shroff says the issue has sure similarities to the machine learning-based recommender systems utilized in e-commerce. “We took the idea from the retail world, the place an information analyst can have a look at receipts and see what gadgets persons are shopping for collectively,” he says. “As a substitute of a transaction receipt, we’ve a novel half identifier and as a substitute of the gadgets {that a} shopper would buy, we’ve an inventory of failing checks.”
The NXP algorithm then found which checks fail collectively. After all, 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 take a look at of an automotive half at a specific temperature means different checks don’t must be carried out. “We have to have 100% or close to 100% certainty,” Shroff says. “We function in a special house with respect to statistical rigor in comparison with the retail world, nevertheless 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 it is smart from engineering perspective and which 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 purposes processors constructed utilizing superior chipmaking processes. Relying on which chip was concerned, they had been topic to between 41 and 164 checks, and the algorithm was capable of suggest eradicating 42 to 74 p.c of these checks. Extending the evaluation to information from different kinds of chips led to an excellent wider vary of alternatives to trim testing.
The algorithm is a pilot venture for now, and the NXP workforce is seeking to increase it to a broader set of components, cut back the computational overhead, and make it simpler to make use of.
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