Statistical circuit design using neural network and orthogonal array

The neural network and orthogonal array are introduced for statistical circuit design. As an alternative to quadratic approximation, a back-propagation neural network is utilized as a classifier and employed to nonlinearly approximate to the feasible region in the circuit element space to improve the accuracy of approximation. The orthogonal array, which has found wide applications in experimental design, is exploited for design centering and speeding up the yield optimization process. An 11-element low-pass filter is given as a design example to show that the efficiency of the new method is higher than that of the quadratic approximation method.<
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