| dc.description |
With the emergence of the deep submicron era , process variations have gained importance in issues related to chip design . The impact of process variations is measured
using manufacturing /parametric yield . In order to get an accurate estimate of yield ,
the parameters considered need to be monitored at a large number of locations . Nowadays , intra -die variations are an integral part of the overall process
uctuations . This
leads to the difficult case where yield prediction has to be done while considering
independent and partially correlated variations . The presence of partial correlations
adds to the existing trouble caused by the volume of variables . This thesis proposes
two techniques for reducing the number of variables and hence the complexity of
the yield computation problem namely - Principal Component Analysis (PCA ) and
Hierarchical Adaptive Quadrisection (HAQ ) . Systematic process variations are also
included in our yield model . The biggest plus in these two methods is reducing the
size of the yield prediction problem (thus making it less time complex ) without affecting the accuracy in yield . The efficiency of these two approaches is measured by
comparing with the results obtained from Monte Carlo simulations . Compared to
previous work , the PCA based method can reduce the error in yield estimation from
17 .1 % - 21 .1 % to 1 .3 % - 2 .8 % with 4 .6x speedup . The HAQ technique can reduce
the error to 4 .1 % - 5 .6 % with 6x - 9 .4x speedup . |
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