Chapter 5 Robust Performance Tailoring with Tuning - SSL - MIT
Chapter 5 Robust Performance Tailoring with Tuning - SSL - MIT Chapter 5 Robust Performance Tailoring with Tuning - SSL - MIT
are shown in Figure 5-4, the final evolution of the design regimes plot introduced at the end of Chapter 3. The y-axis represents the performance requirement of the system and the x-axis is the level of uncertainty in the parameters. The numbered regions indicate areas in which particular design methodologies are successful for all possible uncertainty realizations as listed inthelegend. Forexample,inRegion1, PT, RPT and RPTT all produce designs that can meet the requirement within the uncertainty bounds, while in region 4 only PT tuned and RPTT designs are successful. Performance Requirement [µm] 400 350 300 250 200 150 100 50 1 5 3 4 1: PT, RPT, RPTT 2: PT tuned, RPT, RPTT 3: PT tuned, RPT tuned, RPTT 4: PT tuned, RPTT 5: RPT, RPTT 6: RPT tuned, RPTT 7: RPTT 0 0 5 10 15 20 25 Uncertainty [%] Figure 5-4: Performance requirement vs uncertainty: all designs. In Region 7, only RPTT can produce a successful design indicating that consider- ing tuning in the tailoring stage of the design further extends the uncertainty that can be tolerated at a given performance requirement. For example, at a requirement of σreq = 200µm the maximum uncertainty that can be tolerated without RPTT is 7% and is achieved through a combination of PT design and hardware tuning. However, if RPTT is applied to the system the uncertainty range is extended to 9% for the 166 7 6 2
same requirement. The effect becomes more dramatic as the requirement is relaxed slightly. At a requirement of 280µm the tuned RPT design can tolerate up to 11% uncertainty, but the RPTT design allows ≈ 16.3%. The entire 25% uncertainty range is covered for requirements of 320µm and higher with the RPTT design. This re- quirement is nearly 13% lower (more aggressive) than that which can be met by the tuned RPT design at the same uncertainty level. In addition to extending the reachable design space, RPTT is the only method that is successful in all of the design regions. As noted in the previous chapter, Region 4 is interesting because the tuned PT design is adequate, but RPT and RPT tuned are not. This result is concerning because the uncertainty is relatively low in this region and it is reasonable to assume that RPT or tuned RPT formulations are the correct approach here. However, the plot shows that, for this problem, tuning the PT design achieves a more aggressive performance requirement than tuning the RPT design at the same level of uncertainty. For example, at ∆ = 5.75% the tuned PT design can meet a requirement of 178µm, while the tuned RPT design can only meet a requirement of 218.62µm. In contrast, RPTT is able to meet the same requirement as tuned PT, and can go slightly further to 161.43µm if necessary. In fact, RPTT is the only method that is appropriate in all of the regions shown here. 5.3 RPTT Simulations In this section the RPTT design space is investigated more thoroughly through a series of simulations. First the trade between tuning authority and robustness is con- sidered by varying the robustness weight, α. Then, the PT, RPT and RPTT designs are compared over a set of random hardware simulations to assess the methodology performance the entire uncertainty space. 5.3.1 Tuning Authority Recall from Equation 5.1 that the RPTT objective function includes weighted robust- ness and tuning authority costs allowing a trade of relative importance between the 167
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are shown in Figure 5-4, the final evolution of the design regimes plot introduced<br />
at the end of <strong>Chapter</strong> 3. The y-axis represents the performance requirement of the<br />
system and the x-axis is the level of uncertainty in the parameters. The numbered<br />
regions indicate areas in which particular design methodologies are successful for all<br />
possible uncertainty realizations as listed inthelegend. Forexample,inRegion1,<br />
PT, RPT and RPTT all produce designs that can meet the requirement <strong>with</strong>in the<br />
uncertainty bounds, while in region 4 only PT tuned and RPTT designs are successful.<br />
<strong>Performance</strong> Requirement [µm]<br />
400<br />
350<br />
300<br />
250<br />
200<br />
150<br />
100<br />
50<br />
1<br />
5<br />
3<br />
4<br />
1: PT, RPT, RPTT<br />
2: PT tuned, RPT, RPTT<br />
3: PT tuned, RPT tuned, RPTT<br />
4: PT tuned, RPTT<br />
5: RPT, RPTT<br />
6: RPT tuned, RPTT<br />
7: RPTT<br />
0<br />
0 5 10 15 20 25<br />
Uncertainty [%]<br />
Figure 5-4: <strong>Performance</strong> requirement vs uncertainty: all designs.<br />
In Region 7, only RPTT can produce a successful design indicating that consider-<br />
ing tuning in the tailoring stage of the design further extends the uncertainty that can<br />
be tolerated at a given performance requirement. For example, at a requirement of<br />
σreq = 200µm the maximum uncertainty that can be tolerated <strong>with</strong>out RPTT is 7%<br />
and is achieved through a combination of PT design and hardware tuning. However,<br />
if RPTT is applied to the system the uncertainty range is extended to 9% for the<br />
166<br />
7<br />
6<br />
2