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Semiconductor Testing: Machine Learning Might Mean Less Chip Testing


Completed chips coming in from the foundry are topic to a battery of checks. For these destined for important techniques in automobiles, these checks 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 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 automobile. 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 known as continue-on-fail. In that course of, chips are examined in teams and are all subjected to the whole battery, even when some components fail among the checks alongside the best way.

Chips had been topic to between 41 and 164 checks, and the algorithm was in a position to advocate eradicating 42 to 74 % of these checks.

“We’ve to make sure stringent high quality necessities within the area, so now we have to do quite a lot 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 manage prices. “What we had been attempting to do right here is provide you with a strategy to scale back take a look at value in a manner 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 systems 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 individuals are shopping for collectively,” he says. “As an alternative of a transaction receipt, now we have a novel half identifier and as an alternative of the gadgets {that a} client would buy, now we have a listing 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 completely different from whether or not a take a look at of an automotive half at a selected temperature means different checks don’t have to be executed. “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, but it surely’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 must “be certain that 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 knowledge 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 in a position to advocate eradicating 42 to 74 % of these checks. Extending the evaluation to knowledge from different sorts of chips led to an excellent wider vary of alternatives to trim testing.

The algorithm is a pilot challenge for now, and the NXP staff 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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