An efficient statistical analysis methodology and its application to high-density DRAMs

In this work, a new approach for the statistical worst case of full-chip circuit performance and parametric yield prediction, using both the Modified-Principal Component Analysis (MPCA) and the Gradient Method (GM), is proposed and verified. This method enables designers not only to predict the standard deviations of circuit performances but also track the circuit performances associated with the process shift by measuring E-tests. This new method is validated experimentally during the development and production of high density DRAMs. Our contributions to statistical circuit design are as follows: 1) a method for directly generating a parametrized model associated with electrical test data 2) the first application to high density DRAMs using the true statistical method

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