That would increase the experimental error of each resistivity measurement by the run-to-run furnace variability and make it more difficult to study the effects of the different dosages. However, regular production wafers have furnace priority, and only a few experimental wafers are allowed into any furnace run at the same time.Ī non-blocked way to run this experiment would be to run each of the twelve experimental wafers, in random order, one per furnace run. That would eliminate the nuisance furnace factor completely. The nuisance factor they are concerned with is "furnace run" since it is known that each furnace run differs from the last and impacts many process parameters.Īn ideal way to run this experiment would be to run all the 4x3=12 wafers in the same furnace run. They have four different dosages they want to try and enough experimental wafers from the same lot to run three wafers at each of the dosages. Suppose engineers at a semiconductor manufacturing facility want to test whether different wafer implant material dosages have a significant effect on resistivity measurements after a diffusion process taking place in a furnace. L k = number of levels (settings) of factor k Example of a Randomized Block Design ![]() L 1 = number of levels (settings) of factor 1 L 2 = number of levels (settings) of factor 2 L 3 = number of levels (settings) of factor 3 L 4 = number of levels (settings) of factor 4 One useful way to look at a randomized block experiment is to consider it as a collection of completely randomized experiments, each run within one of the blocks of the total experiment. Randomization is then used to reduce the contaminating effects of the remaining nuisance variables. "Block what you can randomize what you cannot."īlocking is used to remove the effects of a few of the most important nuisance variables. The analysis of the experiment will focus on the effect of varying levels of the primary factor within each block of the experiment.īlock for a few of the most important nuisance factors Within blocks, it is possible to assess the effect of different levels of the factor of interest without having to worry about variations due to changes of the block factors, which are accounted for in the analysis.Ī nuisance factor is used as a blocking factor if every level of the primary factor occurs the same number of times with each level of the nuisance factor. The basic concept is to create homogeneous blocks in which the nuisance factors are held constant and the factor of interest is allowed to vary. When we can control nuisance factors, an important technique known as blocking can be used to reduce or eliminate the contribution to experimental error contributed by nuisance factors. The experimenter will typically need to spend some time deciding which nuisance factors are important enough to keep track of or control, if possible, during the experiment.īlocking used for nuisance factors that can be controlled For example, in applying a treatment, nuisance factors might be the specific operator who prepared the treatment, the time of day the experiment was run, and the room temperature. Nuisance factors are those that may affect the measured result, but are not of primary interest. However, there are also several other nuisance factors. 9 Generalizations of randomized block designsīlocking to "remove" the effect of nuisance factorsįor randomized block designs, there is one factor or variable that is of primary interest. ![]() ![]() ![]() 8 Estimates for a Randomized Block Design.4 Block for a few of the most important nuisance factors.2 Blocking used for nuisance factors that can be controlled.1 Blocking to "remove" the effect of nuisance factors.
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