Chapter 5 Robust Performance Tailoring with Tuning - SSL - MIT
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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

ssl.mit.edu
11.12.2012 Views

Performance [µm] % of Simulations 1200 1000 800 600 400 200 100 90 80 70 60 50 40 30 20 10 0 0 Nominal Tuned PT RPT RPTT (a) Pass Nominal Pass Tuned Fail PT RPT RPTT (b) Figure 5-8: PT, RPT and RPTT simulations results, ∆ = 10%, σzreq = 220µm: (a) performance results (b) design success. 172

than the RPT design, 155.45µm to 551.5µm, but is less sensitive to uncertainty than the PT design. The lower range of the RPTT nominal performance is below the requirement indicating that some of the nominal hardware simulations are successful without tuning. More importantly, the entire tuned range, 148.16µm to 211.23µm, is below the requirement indicating that all of the designs are successful once tuned. The lower subplot, Figure 5-8(b), is a bar chart showing the percent of simulations that are successful, i.e. meets requirements, for each design. Successful designs are broken into two subcategories: those that pass nominally (white bars) and those that pass after tuning (gray bars). The failed designs are indicated by black bars. The PT design is largely successful, with 94.5% of the simulations meeting the requirement. However, the majority of simulations need to be tuned (only 27% pass nominally), and 5.5% of the simulations fail even with tuning indicating that there is no guarantee of success with the PT design. The RPT design fares much worse with a 100% failure rate over the simulations. As discussed in Chapters 3 and 4, the RPT is much less sensitive to uncertainty, but is also insenstive to the tuning parameters resulting in a design with a small range on both nominal performance and tuning. Only the RPTT design is successful for 100% of the simulations. In addition, over half of the RPTT simulations pass nominally, and tuning is only required in 47.5% of the cases. Even though the robust weight, α was set to zero, RPTT achieves a blend of tunability and robustness since the design is tuned at all of the uncertainty vertices. The resulting design is more robust to uncertainty than the PT design and is more likely to meet requirements in the nominal hardware configuration. The results of the simulations at ∆ = 10% are interesting because although RPTT is the only design that is successfull 100% of the time, it is surprising to see that the PT design is highly tunable and largely successful despite its high sensitivity to the uncertainty parameters. To further explore this issue 200 simulations are run with a higher uncertainty level, ∆ = 21.5%. The design regimes in Figure 5-4 indicate that none of the designs can accomodate such a high uncertainty level and a requirement of 220µm, so for these simulations the requirement is relaxed to σreq = 330µm. In addition, two RPTT designs are generated, one with α =0.0 and the other 173

<strong>Performance</strong> [µm]<br />

% of Simulations<br />

1200<br />

1000<br />

800<br />

600<br />

400<br />

200<br />

100<br />

90<br />

80<br />

70<br />

60<br />

50<br />

40<br />

30<br />

20<br />

10<br />

0<br />

0<br />

Nominal<br />

Tuned<br />

PT RPT RPTT<br />

(a)<br />

Pass Nominal<br />

Pass Tuned<br />

Fail<br />

PT RPT RPTT<br />

(b)<br />

Figure 5-8: PT, RPT and RPTT simulations results, ∆ = 10%, σzreq = 220µm: (a)<br />

performance results (b) design success.<br />

172

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