Chapter 5 Robust Performance Tailoring with Tuning - SSL - MIT

two. The effects of this weighting, α, on the design performance predictions are ex-
plored by finding the optimal RPTT design for a range of α at a constant uncertainty
bound, ∆ = 10%. The nominal performance prediction of each design is evaluated
along with the performance at the worst-case uncertainty vertex. The worst-case
realization is then tuned to obtain the tuned performance.
Performance [µm]
600
500
400
300
200
Nominal
Worst−Case
Tuned
100
0 0.2 0.4 0.6 0.8 1
Weighting, α
Figure 5-5: RPTT design performance as a function of weighting (α) for∆=0.10:
(–) requirement.
The results of this study are shown in Figure 5-5. Nominal, worst-case and tuned
performance are depicted by circles, squares and triangles, respectively. At low values
of α the tuning authority is weighed heavier than robustness, and, as expected, the
tuned performance is best in this region. It is interesting to note that the nominal
performance is also very good when α is low, but the worst-case performance is
very high. As α increases, robustness becomes more important, and the worst-case
performance decreases while the nominal and tuned performance increase. It is clear
that, for this model, a trade exists between tuning authority and robustness.
Consider a requirement of 220µm, indicated by a solid black line on the figure.
The tuned performance meets this requirement only at robustness weight close to zero
168