5/27/2023 0 Comments Sequential testing at intel![]() ![]() Crossing one of the boundaries results in stopping the trial with a decision to reject or to accept the null hypothesis. ![]() The boundaries can be maintained even when one deviates from the original design in terms of number and timings of interim analyses. The two functions produce two decision boundaries, an efficacy boundary limiting the test statistic ( z score) from above and a futility boundary limiting it from below. The control of type I errors is achieved by way of an alpha-spending function while control of the type II error rate is handled by a beta-spending function. This also introduces bias and requires the use of bias-reducing / bias-correcting techniques as the sample mean is no longer the maximum likelihood estimate. Implementing a winning variant as quickly as possible is desirable and so is stopping a test which has little chance of demonstrating an effect or is in fact actively harming the users exposed to the treatment.Ī drawback is the increased computational complexity since the stopping time itself is now a random variable and needs to be accounted for in an adequate statistical model in order to draw valid conclusions. The added flexibility in the form of the ability to analyze the data as it gathers is also highly desirable as a form of reducing business risk and of opportunity costs. For example, one can cut down test duration / sample size by 20-80% (see article references) while maintaining error probability. The benefits of a sequential testing approach is the improved efficiency of the test. They can also be performed by using an adaptive sequential design when necessary, although it offers no efficiency improvements and are much more complex. Sequential testing is usually done by using a so-called group-sequential design (GSD) and sometimes such tests are called group-sequential trials (GST) or group-sequential tests. This should not be mistaken with unaccounted peeking at the data with intent to stop. Sequential testing employs optional stopping rules ( error-spending functions) that guarantee the overall type I error rate of the procedure. Sequential testing is the practice of making decision during an A/B test by sequentially monitoring the data as it accrues. The numerical results suggest that screening at the chip level can be highly profitable in a semiconductor manufacturing process constrained by its probe capacity.Aliases: sequential monitoring, group-sequential design, GSD, GST ![]() Chip screening strategies are proposed that exploit the various types of yield nonuniformities that are detected in the data, such as radial effects, spatial clustering of bad chips, and yield variation by chip location. Over 300 wafers from two industrial facilities are analyzed in this paper, and a Markov random field model is employed to capture the spatial clustering of bad chips. These decisions are subject to capacity constraints at both the wafer fabrication and probing facilities. Under this assumption, we consider the problem of choosing the optimal start rate of lots of wafers into the fabrication facility and the optimal chip screening policy in front of the probing facility to maximize the expected profit, which is the revenue from good chips minus the variable fabrication and probing costs. After wafers are fabricated, the individual chips on the wafers are probed, or electrically tested, and, in some cases, the probing facility is the bottleneck for the entire IC manufacturing process. This paper addresses a problem of simultaneous quality and quantity control motivated by semiconductor manufacturing. ![]()
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