By Trent McConaghy
This e-book objectives customized IC designers who're encountering edition matters of their designs, particularly for contemporary procedure nodes at 45nm and less than, similar to statistical technique adaptations, environmental adaptations, and structure results. It teaches them the state of the art in Variation-Aware layout instruments, which aid the fashion designer to research fast the difference results, determine the issues, and attach the issues. additionally, this publication describes the algorithms and set of rules behavior/performance/limitations, that's of use to designers contemplating those instruments, designers utilizing those instruments, CAD researchers, and CAD managers.
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Additional info for Variation-Aware Design of Custom Integrated Circuits: A Hands-on Field Guide: A Hands-on Field Guide
Example text
Fast PVT verification runs only 568 simulations to find the worst-case corners for this design out of the total 3645 combinations of process and environmental conditions. 2 shows the number of simulations performed for each step in this flow. For comparison purposes, two other approaches were used on this design and are also summarized in the table. The first column summarizes the Fast PVT flow, the second column summarizes the flow of running all combinations and the third column uses the designer’s best guess for determining worst-case corners.
Once the sensitivity of the outputs is determined across the worst-case corners, the design is modified and the performance is checked by simulating at each worstcase corner. In this example, this iterative modify/simulate procedure is repeated four times to achieve satisfactory performance across all worst-case corners and to find an acceptable tradeoff between performance, power, and area. After the design iterations are complete, Fast PVT verification is performed. The analysis confirms that design performance is acceptable across the entire range of PVT combinations.
7 PVT verification cast as a global optimization problem using feedback to try more x-values, ultimately maximizing the y-value. In this case, different values of temperature are being selected and simulated to find the maximum value of power. The top of the hill in Fig. 7 right is a local optimum, as none of the nearby x-values have a higher y-value. We do not want the Fast PVT algorithm to get stuck in this local optimum; it should instead find the top of the hill in Fig. 7 left, which is the global optimum.