Yield optimization for nondifferentiable density functions using convolution techniques

A method of yield derivative estimation for nondifferentiable or truncated probability-density functions (PDFs) is proposed and applied to yield optimization. The method applies convolution techniques and is based on the recently introduced perturbation approach. It constructs some approximation to the original PDF and requires a small number of samples per yield-optimization-algorithm step. The method is efficient and provides fast convergence in the solution, especially for problems of high dimensionality. Several yield-gradient estimation formulas are given. Some theoretical and practical aspects of the proposed method are discussed. Practical applications are demonstrated on several analog filters, and the method is compared with some other existing methods