Completed chips coming in from the foundry are topic to a battery of checks. For these destined for important programs in automobiles, 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 check?
Engineers at NXP have developed a machine-learning algorithm that learns the patterns of check outcomes and figures out the subset of checks which are actually wanted and those 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 complicated circuitry and superior chip-making know-how, together with inverters for EV motors, audio chips for shopper electronics, and key-fob transponders to safe your automotive. These chips are examined with completely different indicators at completely different voltages and at completely different temperatures in a check course of known 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 checks alongside the way in which.
Chips have been topic to between 41 and 164 checks, and the algorithm was capable of advocate eradicating 42 to 74 p.c of these checks.
“Now we have to make sure stringent high quality necessities within the area, so we have now to do a number 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 corporations, testing is without doubt one of the few knobs most chip corporations can flip to regulate prices. “What we have been making an attempt to do right here is provide you with a method to cut back check value in a means 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 programs utilized in e-commerce. “We took the idea from the retail world, the place a knowledge analyst can take a look at receipts and see what gadgets persons are shopping for collectively,” he says. “As an alternative of a transaction receipt, we have now a singular half identifier and as an alternative of the gadgets {that a} shopper would buy, we have now an inventory of failing checks.”
The NXP algorithm then found which checks fail collectively. In fact, what’s at stake for whether or not a purchaser of bread will need to purchase butter is sort of completely different from whether or not a check of an automotive half at a specific temperature means different checks don’t have to be achieved. “We have to have 100% or close to 100% 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. It’s important to “ensure that it is smart 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 purposes processors constructed utilizing superior chipmaking processes. Relying on which chip was concerned, they have been topic to between 41 and 164 checks, and the algorithm was capable of advocate eradicating 42 to 74 p.c of these checks. Extending the evaluation to information from different kinds 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 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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